\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"},{slug:"suf-and-intechopen-announce-collaboration-20200331",title:"SUF and IntechOpen Announce Collaboration"}]},book:{item:{type:"book",id:"1533",leadTitle:null,fullTitle:"Nd YAG Laser",title:"Nd YAG Laser",subtitle:null,reviewType:"peer-reviewed",abstract:"Discovered almost fifty years ago at Bell Labs (1964), the Nd:YAG laser has undergone an enormous evolution in the years, being now widely used in both basic research and technological applications. Nd:YAG Laser covers a wide range of topics, from new systems (diode pumping, short pulse generation) and components (a new semiorganic nonlinear crystal) to applications in material processing (coating, welding, polishing, drilling, processing of metallic thin films), medicine (treatment, drug administration) and other various fields (semiconductor nanotechnology, plasma spectroscopy, laser induced breakdown spectroscopy).",isbn:null,printIsbn:"978-953-51-0105-5",pdfIsbn:"978-953-51-4960-6",doi:"10.5772/2000",price:139,priceEur:155,priceUsd:179,slug:"nd-yag-laser",numberOfPages:330,isOpenForSubmission:!1,isInWos:1,hash:"db0c2af1b51a92668c5ab54719f12745",bookSignature:"Dan C. 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He obtained his PhD at the Institute of Atomic Physics, Bucharest in 1978. Since 1970 he has been involved in research on laser physics and applications (frequency stabilization of lasers, photoacoustic spectroscopy, laser applications in medicine and biology, material processing and ultrashort pulse, high intensity lasers - extreme light) at the Department of Lasers, Institute of Atomic Physics and at the National Institute for Laser, Plasma and Radiation Physics, Bucharest. He works as a professor at Faculty of Physics, University of Bucharest and as a professor and a PhD supervisor at the Faculty of Applied Sciences, University Politehnica, Bucharest. He is the author and/or editor of 15 books and has published more than 120 papers in scientific journals. He held more than 200 presentations at the international conferences (30 invited lectures). 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\r\n\tCrowdfunding can be defined as the collective effort of many individuals who create a network and pool their resources to support projects initiated by other people or organizations, usually through or with the help of the Internet. Individual projects or companies are financed with small contributions by a large number of individuals, allowing innovators, entrepreneurs, and business owners to use their social networks to raise capital.
\r\n\r\n\tCrowdfunding is therefore a sort of business model thanks to which a company or a public administration (including health) entrusts the creation phases of a product or service, from its design to its realization, to a set of individuals (the crowd), through an open call, to which anyone can respond by adding value of their technical and commercial skills. Compared to the traditional form of outsourcing of business activities, crowdfunding not only allows high cost savings but generates a significant benefit in terms of social and innovative capital to which the company can draw for the development of its projects.
\r\n\r\n\tThis book will offer, through its authors, in a clear and easy-to-read style, comprehensive coverage of the various aspects of crowdfunding. The text will focus on real core issues, which are the tools for appraising the performance of an organization that adopts crowdfunding.
\r\n\r\n\tIn this context, the book intends to provide the reader with a comprehensive overview of the current state of the art in crowdfunding.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"18027",title:"Classification of Emotional Stress Using Brain Activity",doi:"10.5772/18294",slug:"classification-of-emotional-stress-using-brain-activity",body:'Stress and Emotion are complex phenomena that play significant roles in the quality of human life. Emotion plays a major role in motivation, perception, cognition, creativity, attention, learning and decision-making (Seymour et al., 2008). A major problem in understanding emotion is the assessment of the definition of emotions. In fact, even psychologists have problem agreeing on what is considered an emotion and how many types of emotions exist. Kleinginna gathered and analyzed 92 definitions of emotion from literature present that day. He concludes that Emotion is a complex set of interactions among subjective and objective factors, mediated by neural/hormonal systems (Horlings, 2008). In fact, Emotion is a subcategory of stress.
A lot of research has been undertaken in assessment of stress and emotion over the last years. Most of researches in the domain of stress and emotional states use peripheral signals such as respiratory rate, Skin Conductance (SC), Blood Volume Pulse (BVP) (Zhai et al., 2006) and Temperature (McFarland, 1985). Most previous research, have investigated the use of EEG and peripheral signals separately, but little attention has been paid so far to the fusion between EEG and peripheral signals (Chanel, 2009; Chanel et al., 2009;\n\t\t\t\tHosseini, 2009).
In one study, Aftanas et al. (2004) that showed significant differentiation of arousal based on EEG data collected from participants watching high, intermediate and low arousal images. Chanel (2009) asked the participants to remember past emotional episodes, and obtained the accuracy of 88% using EEG for 3 categories with Support Vector Machine (SVM) classifier. Hosseini et al. (2009) used the induction visual images based acquisition protocol for recording the EEG and peripheral signals under 2 categories of emotional stress states (Calm-neutral and Negatively-exited) of participants, and obtained the accuracy of 78.3% using EEG signals with SVM classifier. Kim et al. (2004) used the combination of music and story as stimuli and there were 50 participants, to introduce a user independent system, the results showed the accuracy of 78.4% and 61% for 3 and 4 categories of different emotions respectively. Takahashi (2004) used film clips to stimulate participants with five different emotions, resulting in 42% of correctly identified patterns. Schaaff & Schultz (2009) used pictures from the International Affective Picture System (IAPS) to induce three emotional states: pleasant, neutral, and unpleasant. They obtained the accuracy of 66.7% for three classes of emotion, solely based on EEG signals.
The aim of this chapter is to produce a new fusion between EEG and peripheral signals for emotional stress recognition. Since ElectroEncephaloGram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research, it is used as the main signal. Brain waves occur during the activity of brain cells and have a frequency range of 1 to 100 Hz. Researchers have found that the following are the frequency bands of interest to interpret EEG signal: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) and gamma (> 30 Hz) (Ko et al., 2009).
One of the recent weaknesses, lack of proper channels selected brain signals are recorded. In this study, in order to choose the proper EEG channels, a cognitive model of the brain under emotional stress has been used (Hosseini et al., 2010a).
Every standard test in stress and emotion assessment has its own advantages and disadvantages (Hosseini, 2009). A efficient acquisition protocol was designed to acquire the EEG signals in five channels and peripheral signals such as Blood Volume Pulse (BVP), Skin Conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants The visual stimuli images were selected from the subset IAPS database (Lang et al., 2005).
An important issue in every cognitive system is the correct labelling of the data. Here, labelling means the assessment of the data using a series of visual criteria used by psychologists and a proposed cognitive system for peripheral signals in order to verify the existence of a close correlation of the data and the psychological state of the subject. In this kind of research, putting the subject in the desired psychological state is very important. The process of labelling EEG signals consists of three stages: first self-assessment, second the qualitative analysis of peripheral signals and third the quantitative analysis of peripheral signals. Therefore, this new fusion link between EEG and peripheral signals are more robust in comparison to the separate signals.
Cognitive models (also termed agent architectures) aim to emulate cognitive processing such as attention, learning, perception, and decision-making, and are used by cognitive scientists to advance understanding of the mechanisms and structures mediating cognition (Hudlicka, 2005). In the case of fear conditioning leading to emotional stress, several hypotheses have been proposed to explain how neural changes occur in the different components in a circuit leading to the observed behavioural responses. In mammalians, a part of the brain, called the limbic system, is mainly responsible for emotional processes (Xiang, 2007). We are going to describe developing a cognitive model of the limbic system based on these concepts. The main components of the limbic system involved in emotional stress processes are amygdala, orbito-frontal cortex, thalamus, sensory cortex, hypothalamus, hippocampus and some other of important areas. In this section, we are trying to briefly describe these components and their tasks. The amygdala, a small structure in the temporal lobes, plays a central role in emotion. It is generally accepted that the amygdala is crucial for the acquisition and expression of conditioned fear responses (for a review, see (LeDoux, 1996)). The amygdala and orbito-frontal cortex receive highly analyzed input from the sensory cortex. The amygdala, specifically its lateral nucleus, receives inputs from all the main sensory systems, as well as from higher-order association areas of the cortex and the hippocampus (Armony et al., 1997). Sensory information reaches the amygdala from the thalamus by the way of two parallel pathways: the direct pathways reach the amygdala quickly, but they are limited in their information content, as the thalamic cells of origin of the pathway are not very precise stimulus discriminators. The cortical pathway, on the other hand, is slower but capable of providing the amygdala with a much richer representation of the stimulus (Armony et al., 1997). The sensory cortex is the component next to the thalamus and receives its input through this component. The orbito-frontal cortex is another component, which interacts with the amygdala reciprocally. The orbito-frontal cortex also plays a role in reinforcement learning of emotions. The term prefrontal cortex refers to the very front of the brain, behind the forehead and above the eyes. It appears to play a critical role in the regulation of emotion and behaviour by anticipating the consequences of our actions. The prefrontal cortex may play an important role in delayed gratification by maintaining emotions over time and organizing behaviour toward specific goals (Xiang, 2007). The locus ceruleus contains a large proportion of the noradrenalin cell bodies found in the brain and it is a key brain stem region involved in arousal (Steimer, 2002). The Bed Nucleus of the Stria Terminalis (BNST) is considered part of the extended amygdala. It appears to be a centre for the integration of information originating from the amygdala and the hippocampus, and is clearly involved in the modulation of the neuroendocrine stress response (Steimer, 2002). The hypothalamus (ParaVentricular Nucleus (PVN) and Lateral Hypothalamus (LH)) lies below the Thalamus and it is believed to have various functions that regulate the endocrine system, the autonomous nervous system and primary behavioural surviving states (Steimer, 2002; Schachter, 1970). The Hypothalamic-Pituitary-Adrenal (HPA) axis ultimately regulates the secretion of glucocorticoids, which are adrenocortical steroids that act on target tissues throughout the body in order to preserve homeostasis during stress (Ramachandran, 2002). The hypothalamus also releases Corticotrophin-Releasing Hormone (CRH), which travels to the anterior pituitary gland, where it triggers the release of AdrenoCorticoTropic Hormone (ACTH), which, along with β-endorphin, is released into the bloodstream in response to stress. ACTH travels in the blood to the adrenal glands, where it stimulates the production and release of glucocorticoids such as cortisol. Cortisol feedback at the hypothalamus reduces CRF release. At the pituitary, it inhibits ACTH release, and at the adrenal gland, it inhibits further cortisol release. Cortisol feedback at the hippocampus inhibits CRF secretion from the hypothalamus. The release of all these chemicals causes important changes in the body’s ability to respond to threats such as increased energy, heart rate and blood sugar resulting in increased arousal and pain relief (Carey, 2006). In order to choose the best channels for EEG signals, we implemented a new cognitive model (for a review of complete model, see (Hosseini et al., 2010a; Hosseini et al., 2010d)). The detail is shown in Fig. 1. (Hosseini et al., 2010a).
Every standard test in stress and emotional states assessment has its own advantages and disadvantages (Hosseini, 2009). Most experiments that measure emotion from EEG signals use pictures from the International Affective Picture System (IAPS). The IAPS evaluated by several American participants on two dimensions of nine points each (1-9). The use of IAPS allows better control of emotional stimuli and simplifies the experimental design (Horlings, 2008).
A general cognitive map of brain for stress state
In this study, we chose the picture presentation test, base on the closeness of its assessment to our aims. The stimuli to elicit the target emotions (calm-neutral and negatively excited) were some of the pictures (http://www.unifesp.br/dpsicobio/adap/exemplos_fotos.htm). The valence dimension ranging from negative to positive and the arousal dimension, ranging from calm to excited. Information about both dimensions has been found to be present in EEG signals, which shows that emotion assessment from EEG signals should be possible.
The participant sits in front of a portable computer screen in a bare room relatively, the images to inform him about the specific emotional event he has to think of. Each experiment consists of 8 trials. Each stimulus consists of a block of 4 pictures, which ensures stability of the emotion over time. In addition, each picture is displayed for 3 seconds leading to a total 12 seconds per block. Prior to displaying images, a dark screen with an asterisk in the middle is shown for 10 seconds to separate each trial and to attract the participant’s attention. The detail of each trial is shown in Fig. 2.
The protocol of data acquisition
This epoch duration was chosen because to avoid participant fatigue. In Fig. 3, each presentation cycle started with a black fixation cross, which was shown for ten seconds. After that pictures were presented for twelve seconds.
Process of picture presentation
Fifteen healthy volunteered subjects were right-handed males between the age of 20 and 24 years. Most subjects were students from biomedical engineering department of Islamic Azad University- Mashhad Branch. Each participant was examined by a dichotic listening test to identify the dominant hemisphere (Sadock, 1998; Hosseini, 2009). All subjects had normal or corrected vision; none of them had neurological disorders. These were done to eliminate any differences in subjects. All participants gave written informed consent. Then each participant was given a particulars questionnaire. During the pre-test, several questionnaires have been evaluated in order to check the best psychological input to start the protocol phase; this test is State-Trait Anxiety Inventory (STAI). At the end of the experiment, participants were asked to fill in a questionnaire about the experiment and give their opinions (Hosseini, 2009), because, it is possible that the emotion that a participant experiences differs from the expected value. For that reason, the participant is asked to rate his emotion on a self-assessment.
We used a 10 channel Flexcom Infiniti device, with 14-bit resolution for data acquisition (http://www.thoughttechnology.com/flexinf.htm). It is connected to a PC using the USB port. An optical cable connects to device, to prevent any electrical charge from reaching the participant. The Flexcom Infiniti hardware only worked well with the accompanying software. Two programs were available, Biograph Infiniti Acquisition and ezscan. The central activity is monitored by recording EEGs. The peripheral activity is assessed by using the following sensors: a skin conductance sensor to measure sudation; a respiration belt to measure abdomen expansion; a plethysmograph to record blood volume pulse. We recorded SC by positioning two dedicated electrodes on the top of left index and middle fingers. The sample rate of the BVP and SC signals acquisition was 2048 Hz and then down-sampled to 128 Hz and respiration signal acquisition was 256 Hz and then down-sampled to 128 Hz. For reduce of calculation volume, were implemented the down sampling on BVP and SC signals. EEG was recorded using electrodes placed at 5 positions. The scalp EEG was obtained at location FP1, FP2, T3, T4 and Pz, as defined by the international 10-20 system and Ag/AgCl electrodes. In order to measure a reference signal that is (as much as possible) free from brain activity, we have two electrodes to attach to the participants earlobes. Average of A1 and A2 was used as reference. Impedance of all electrodes was kept below 5 KΩ. The sample rate of the EEG signal acquisition was 256 Hz. Each recording lasted about 3 minutes. More details of the data acquisition protocol can be found in (Hosseini, 2009).
An important issue in every cognitive system is the correct labelling of the data. In order to choose the best emotional stress correlated EEG signals, we implemented a new emotion-related signal recognition system, which has not been studied so far (Hosseini, 2009;\n\t\t\t\tHosseini et al., 2010c). We recorded peripheral signals concomitantly in order to firstly recognize the correlated emotional stress state and then label the correlated EEG signals. In other words, we used the peripheral signals as a tutor for labeling system.
The process of labeling EEG signals consists of three stages: first self-assessment, second the qualitative analysis of peripheral signals and third the quantitative analysis of peripheral signals. Fig. 4 shows the different stages of the process. After the experiment, there was also a self-assessment stage, which is a good way to have an idea about the emotional stimulation “level” of the subject because emotions are known to be very subjective and dependent on previous experience (Savran et al., 2006). In this research, we will be able to get a general idea of the quality of the data, i.e. if the data are good or bad.
One kind of this data is respiration. Emotional stress processes influence respiration (Ritz et al., 2002;\n\t\t\t\tWilhelm et al., 2006). Slow respiration, for example is linked to relaxation while irregular rhythm, quick variations, and cessation of respiration correspond to more aroused emotions like anger or fear. Another one is skin conductance, which measures the conductivity of the skin. Since sweat gland activity is known to be controlled by the sympathetic nervous system, Electro Dermal Activity (EDA) has become a common source of information to measure the Autonomic Nervous System. SC increases if the skin is sweaty, for example, when one is experimenting emotions such as stress. Moreover, blood pressure and Heart Rate Variability (HRV) are variables that correlate with defensive reactions, pleasantness of a stimulus, and basic emotions. We obtained Heart Rate (HR) signal using BVP signal recorded by a plethysmograph. A method to determine HR from a BVP signal is proposed in (Wan & Woo, 2004). Analysis of HRV provides an effective way to investigate the different activities of ANS, an increase of HR can be due to an increase of the sympathetic activity or a decrease of the parasympathetic activity. Two frequency bands (HR spectrum) are generally considered for HR signal, a Low Frequency (LF) band ranging from 0.05 Hz to 0.15 Hz and a High Frequency (HF) band including frequencies between 0.15 Hz and 1 Hz (Hosseini, 2009). In order to analyze the peripheral signals quantitatively, we need to pre-process them, to remove environmental noises by applying filters. The peripheral signals were filtered by moving average filters to remove noise.
Labeling process of EEG signals
We used a common set of feature values for analysis of the peripheral signals (Table 1) (Chanel et al., 2009; Hosseini, 2009). The respiration features are from time and frequency domains, the skin conductance features and the blood volume pulse features are from time domain, and the heart rate variability features are from time, frequency domains and fractal dimension.
Features Extracted from peripheral signals
The total number of features is: [10+9+15+6=40]. After extracting the features, we need to classify them using a classifier. There are several approaches to apply the SVM for multiclass classification (http://www.kernel-machines.org/software.html). The LibSVM MATLAB toolbox (Version 2.9) was used as an implementation of the SVM algorithms (Chang & Lin, 2009). In this study, the one-vs.-all method was implemented. Two SVMs that correspond to each of the two emotions were used. The ith SVM was trained with all of the training data in the ith class with calm labels, and the other training data with negative labels.
In the emotional stress recognition process, the feature vector was simultaneously fed into all SVMs and the output from each SVM was investigated in the decision logic algorithm to select the best emotional stress (Fig. 5). In the SVM classifier, was used a Gaussian Radial Basis function (RBF) as a kernel function. RBF projects the data to a higher dimension.
Decision logic algorithm
A confusion matrix will also be used to determine how the samples are classified in the different classes. A confusion matrix gives the percentage of samples belonging to class ωi and classified as class ωj. The accuracy can be retrieved from the confusion matrix by summing its diagonal elements Pi,i weighted by the prior probability P(ωi) of occurrence of the class ωi. The confusion matrices results of the SVM used for classification of the peripheral signals under two emotional stress states is given in Table 2.
The confusion matrices across participants using peripheral signals using RBF kernel of SVM
The results show that, the classification accuracy with peripheral signals was 76.95% for the two categories, using SVM classifier with RBF kernel.
The numbers of rejected trials that are badly classified that is lower than the number of correctly classified. The percentage of rejected trials is 11%. Method at this stage it has been used to select suitable segments of EEG signal for improving accuracy of signal labeling according to emotional stress state. More details of the labeling process can be found in (Hosseini, 2009).
Before analysis, we first remove the data segment, which contains obvious eye blinking. We need to pre-process EEG signals in order to remove environmental noises and drifts. The data was filtered using a band pass filter in the frequency band of 0.5~60 Hz. Although we studed the EEG signals of up to 30 Hz, we included the 30 to 60 Hz bandwidth, because we need a double maximum frequency content when analyzing the data using HOS (Hosseini, 2009). The signals were filtered using the “filtfilt” function from the signal processing in MATLAB toolbox, which processes the input signal in both forward and reverse directions. This function allows performing a zero-phase filtering. Safety of signal phase information is very important in higher order spectra (Hosseini, 2009). In addition, a notch filter at 50 Hz was placed to discard the effect of power lines.
First time, phase space introduced by W. Gibbs in 1901, that in this space all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space. In fact, all these unique points will make direction of trajectory. A sketch of the phase portrait may give qualitative information about the dynamics of the system. The method is based on the operated result numerically in the EEG dynamics system, the phase trajectory portrait is drawn out in the phase space with the time variance, and the course portrait of the state variables is drawn out with the time (Jiu-ming et al., 2004). The chaotic phenomena and the solution fraction are decided through comparison, analysis and integration. In the phase space, the close curve is corresponding to the periodical motion, while the chaotic motion is corresponding to the ever-non-close trajectory (strange attractor), which diverges randomly in some area, the corresponding figure is as following Fig. 6.
Phase space state portraits for T3 channel of EEG signal for one participant in negative emotional stress state
Feature extraction is the process of extracting useful information from the signal. We use a set of feature values for brain signals. Features are extracted for each channel of EEG signals. Since brain signals essentially have a chaotic and nonlinear behaviour, we performed emotional stress state assessment using both linear and nonlinear characteristics. Nonlinear measures have received the most attention in comparison with the measures mentioned before, for example time domain, frequency domain and other linear features. The nonlinear set of features used includes fractal dimension, approximate entropy and correlation dimension of the data.
Fractal dimension (FD) analysis is frequently used in biomedical signal processing, including EEG analysis. Higuchi’s algorithm unlike many other methods requires only short time intervals to calculate fractal dimension. This is very advantageous, because EEG signal remains stationary during short intervals and because in EEG analysis it is often necessary to consider short, transient events.
In Higuchi’s algorithm (Higuchi, 1988), k new time sequence are constructed from the signal x(1), x(2),…, x(N) under study:
Where m = 1, 2, …, k and k indicate the initial time value, and the discrete time interval between points, respectively. For each of the k time series xkm, the length Lm(k) is computed by:
Where N is the total length of the data sequence x, (N-1)/[(N-m)/k]k is a normalization factor and k average length is computed as the mean of the k lengths Lm(k) for m =1,2,..., k. This procedure is repeated for each k ranging from 1 to max k, obtaining an average length for each k. In the curve of ln(Lm(k)) versus ln(1/k), the slope of the least-squares linear best fit is the estimate of the fractal dimension.
In this research, the best results were obtained for estimating the FD of the EEG; kmax = 10, rectangular window size, N = 512 samples (2 seconds) and window overlap = 0%.
Correlation dimension (D2) is one of the most widely used measures of a chaotic process. In this reasearch, we used the Grassberger and Procaccia Algorithm (GPA) for estimating D2 (Grassberger & Procaccia, 1983). The choice of an appropriate time delay τ and embedding dimension m is important for the success of reconstructing the attractor with finite data. The idea is to construct a function C(r) that is the probability that two arbitrary points on the orbit are closer together than r, where r is the radius of the sphere in the multidimensional space. This is done by calculating the separation between every pair of N data points and sorting them into bins of width dr proportionate to r. More precisely GPA computes the correlation integral C(r) given by,
Where ||V(j)-V(i)|| is the distance between the points V(j) and V(i) and Θ(.) is the heaviside function. D2 is estimated as the slope of the log(C(r)) vs. log(r) graph as follows:
A schematic graph of the correlation dimension plotted as a function of the embedding dimension. When the embedding dimension is equal to or greater than twice the dimension of the state space attractor (dsat) the correlation dimension became independent of m. The correlation dimension of the attractor in this case is about 8.
We calculated D2 with dsat values varying from 2 to 10 for all the subjects. It can be seen that D2 saturates after the embedding dimension of 7 (Fig. 7). Therefore, we have chosen dsat=8 for constructing the embedding space and estimation of the invariants (In the test, m = 8 and τ = 6).
The determination is based on calculating the relative number of pairs of points in the phase-space set that is separated by a distance less than r. For a self-similar attractor, the local scaling exponent is constant, and this region is called a scaling region. This scaling exponent can be used as an estimate of the correlation dimension. If the dsat=8 plots C(N, r) vs. r on a log-log scale, the correlation dimension is given by the slope of the log(C(r)) vs. log(r) curve over a selected range of r, and the slope of this curve in the scaling region is estimated by the least slope fitting (Fig. 8).
A plot of log (C(r)) versus log(r) for logistic map data
Pincus introduced the first idea of approximate entropy (ApEn) in 1991, and it is a useful complexity measure for biological time series data (Pincus, 1991). ApEn is originated from nonlinear dynamics. ApEn is a statistical instrument initially designed to be applied to finite length and noisy time series data, it is scale invariant and model independent, evaluates both dominant and subordinate patterns in data, and discriminates series for which clear feature recognition is difficult. Notably it detects changes in underlying episodic behaviour not reflected in peak occurrences or amplitudes. To understand the concept of ApEn better, we describe the definition step by step as follows: Let the original data be <X(n)> = x(1), x(2),…, x(N), where N is the total number of data points. The calculation of ApEn of signal of finite length is performed as follows. First, fix a positive integer m and a positive real number rf. Next, form the signal x the N-m+1 vector, defined by (5).
The quantity is calculated (6).
Where the distance between the vectors and is defined as (7).
Next, the quantity is calculated as (8).
Increase the dimension to m+1. Repeat steps (1)~(4) and find
In actual operation, the number of data point is limited when the data length is N and the result obtained through the above steps is the estimate of ApEn, which can be denoted as (10)
Obviously, the value of the estimate depends on m and rf. The parameter rf corresponds to an a priori fixed distance between the neighboring trajectory points; therefore, rf can be viewed as a filtering level and the parameter m is the embedding dimension determining the dimension of the phase space. As suggested by Pincus, rf is chosen according to the signal’s standard deviation (SD); in this paper we use the values rf =0.2 SD and m=2 with SD taken over the signal segment under consideration.
Discrete Wavelet Transform (DWT) based feature extraction has been successfully applied with promising results in physiological pattern recognition applications (Murugappan et al., 2009). Choice of suitable wavelet and the number of levels of decomposition is very important in analysis of signals using DWT. In this study, we used Daubechies wavelet function with order db4 for extracting the statistical feature from the EEG signal (Murugappan et al., 2009). The number of levels of decomposition is chosen based on the dominant frequency components of the signal. The levels are chosen such that those parts of the signal that correlate well with the frequencies required for classification of the signal are retained in the wavelet coefficients. Since the EEG signals do not have any useful frequency components above 32 Hz, the number of levels was chosen to be 5. Thus the signal is decomposed into the details D1-D5 and one final approximation, A5. The range of various frequency bands are shown in Table 3.
Frequencies corresponding to different levels of decomposition for “db4” wavelet with a sampling frequency of 256 Hz
The extracted wavelet coefficients provide a compact representation that shows the energy distribution of the EEG signal in time and frequency. Table 2 presents frequencies corresponding to different levels of decomposition for db4 wavelets with a sampling frequency of 256 Hz. It can be seen from table 2 that the components A5 are within the delta (0-4 Hz), D5 are within the Theta (4-8 Hz), D4 are within the alpha (8-13 Hz) and D3 are within the beta (13-30 Hz). Lower level decompositions related to higher frequencies have negligible magnitudes in a normal EEG. In order to, further diminish the dimensionality of the extracted feature vectors; statistics over the set of the wavelet coefficients was used.
Mean of the absolute values of the wavelet coefficients in each sub-band
Average power of the wavelet coefficients in each sub-band
Standard deviation of the wavelet coefficients in each sub-band
These features are extracted for each channel, so the total number of features by this method is: [3×4] = 12.
We analyzed the EEG signal using higher order spectra that are spectral representations of higher order moments or cumulants of a signal (Hosseini et al., 2010b). In this part of paper, we studied features related to the third order statistics of the signal, namely the bispectrum. The bispectrum is a complex quantity, which has both magnitude and phase. The bispectrum is the Fourier transform of the third order correlation of the signal and is given by,
Where * denotes complex conjugate, X(f) is the Fourier transform of the signal x(nT) and E[.] stands for the expectation operation. This method is known as direct Fast Fourier Transform (FFT) based method (Nikias & Mendel, 1993). There is also another indirect method, which is used in this study. For more details on this method please refer to (Hosseini et al., 2010b;\n\t\t\t\t\t\tSwami et al., 2000). If the bispectrum of a signal is zero, none of the wave components are coupled to each other.
Assuming that there is no bispectral aliasing, the bispectral of a real-valued signal is uniquely defined with the triangle f2 ≥ 0, f1 ≥ f2 and f1+f2 ≤ π. For real processes, since discrete bispectrum has symmetric characteristics, it has 12 symmetry regions in the (f1, f2) plane (Swami et al., 2000). Some of these regions can be seen in (12):
The normalized bispectrum (or bicoherence) is defined as
Where P(f1) is the power spectrum.
Since bispectrum and bicoherence cannot fully help signal extraction, Hinich has developed algorithms to test for non-skewness (called Gaussianity) and linearity (Hinich, 1982). The basic idea is that if the third-order cumulants of a process are zero, then its bispectrum is zero, and hence its bicoherence is zero. If the bispectrum is not zero, then the process is non-Gaussian; if the process is linear and non-Gaussian, then the bicoherence is a non-zero constant (Hosseini et al., 2010b).
The Gaussianity test (actually zero-skewness test) involves deciding whether the expected value of the bicoherence is zero, that is, E{Bic(f1,f2)}=0. The test of Gaussianity is based on the mean bicoherence power,
The squared bicoherence is chi-squared distributed (х2 distributed) with two degrees of freedom and non-centrality parameter Lambda (λ) (Swami et al., 2000). In (14) the squared bicoherence is the sum of P points in the non-redundant region, S is the estimated statistics for the Gaussianity test with chi-squared distributed and 2P degree of freedom, and Pfa is the probability of false alarm in rejecting the Gaussian hypothesis. More details can be found in (Hosseini et al., 2010b; Swami et al., 2000). In order to calculate these features, we used a 256 sample FFT with a default C parameter of 0.51. Based on these, the 2P degree of freedom will be 96. The analysis was done using the Higher Order Spectral Analysis (HOSA) toolbox (Swami et al., 2000). The bicoherence was computed using the direct FFT method in the toolbox.
For the whole bifrequency plane region, four quantities were calculated: sum of the bispectrum magnitudes, sum of the squares of the bispectrum magnitudes, sum of the bicoherence magnitudes, and sum of the squares of the bicoherence magnitudes.
The different regions used for analysis in bifrequency plane
Since bispectrum and bicoherence are functions of f1 and f2, in order to define the features, we will have five frequency intervals on each axis, as can be seen in Fig. 9. We will have 15 distinct regions. Then the defined features will be analyzed in each of these 15 regions and in the whole frequency range. These and the three other features obtained from Hinich’s tests for Gaussian and linearity add up to make 7 features for each channel.
These seven features are extracted for each channel, so the total number of features by this method is: [5×4×(15+1)] + [5×3] = 335. The contour plots of the indirect estimate of the bispectrum are shown as examples for T3 channel in Figs. 10 and 11.
A contour plot of the magnitude of the indirect estimated bispectrum on the bifrequency plane, for T3 in calm state
A contour plot of the magnitude of the indirect estimated bispectrum on the bifrequency plane, for T3 in negative emotional stress state
In order to normalize the features in the limits of [-1,1], we used (15).
Here Ynorm is the relative amplitude.
The feature vector presented contains 82 features for a channel EEG recorded over a period of 2 seconds. This leads to the problem of dimensionality, which is solved by in this section until some of the best features should be selected. This is of interest to improve the computational speed of the classification algorithm. Several methods of selecting appropriate features exist. One of the methods described is Genetic Algorithm (GA) (Haupt et al., 2004). The emphasis on using the genetic algorithm for feature selection is to reduce the computational load on the training system while still allowing near optimal results to be found relatively quickly. The GA uses populations of 100 sizes, starting with randomly generated genomes. The probability of mutation was set to 0.01 and the probability of crossover was set to 0.4. The classification performance of the trained network using the whole dataset was returned to the GA as the value of the fitness function, Fig. 12. We attempted to detect the feature sets related to negative/calm emotion response from EEG signal.
Combination of GA and SVM to achieve the best features
We used genetic algorithm in assessment of all the features because a perfect feature group is not necessarily achievable by simply putting a few superior features since the data characteristics and features may have overlapping.
After extracting the desired good features, we still have to find the related emotional stress states in the EEG. A classifier will do this process. In this research, we have used both a static and a dynamic classifier and we will explain them.
Support vector machines are maximum margin classifiers that try to maximize the distance between the decision surface and the nearest point to this surface. Nonlinear support vector machine, maps the input space to a high dimensional feature space, and then constructs a linear optimal hyper plane in the feature space, which relates to a nonlinear hyper plane in the input space. The major problem of training a learning machine to perform supervised classification is to find a function (kernel function) that can not only capture the essential properties of the data distribution, but also prevent the over-fitting problem. We used three kernel functions including linear, polynomial and radial basis function kernels. The C parameter that regulates the trade off between training error minimization and margin maximization is empirically set to1 in this study.
Elman Neural Network (ENN) is a two-layer backpropagation network, with the addition of a feedback connection from the output of the hidden layer to its input. This feedback path allows Elman network to learn to recognize and generate temporal patterns, as well as spatial patterns. The Elman network has tansig neurons in its hidden (recurrent) layer, and purelin neurons in its output layer. This combination is special in that the two-layer networks with these transfer functions can approximate any function (with a finite number of discontinuities) with arbitrary accuracy. The only requirement is that the hidden layer must have enough neurons (Demuth et al., 2008). In this part, the numbers of input neurons are to number optimum features, the numbers of output neurons is 1 and empirically the numbers of hidden neurons is chosen 8. Hence, sigmoid function has been applied for the hidden and output layers, because the sigmoid function is nonlinear and differentiable. The Levenberg-Marquardt back-propagation algorithm is used for training. The Levenberg-Marquardt algorithm will have the fastest convergence compared with other training functions. The error ratio for stop training was considered 0.001.
In this research, we used a 2 seconds time intervals rectangular window without overlap, corresponding to blocks of 512 samples of EEG signals for data segmentation. In classification is important that the training set contain enough samples (or instances). On the other hand, it also important that the test set contains enough samples to avoid a noisy estimate of the model performance. We used around 75% of the EEG signals for the training, and 15% of the data for testing whether the learned relationship between the data and emotional stress is correct and the last 10% was used for validating the data. The results show that, the average classification accuracy with EEG signals were 84.6% and 83.1% for the 2 categories (calm-neutral vs. negatively excited), using the SVM and ENN classifiers respectively. This is particularly true in our case since the number of emotional stimulations is limited by the duration of the protocols, which should not be too long to avoid participant fatigue as well as elicitation of undesired emotions. Cross-validation methods help to solve this problem by splitting the data in different training/test sets so that each sample will be used at least once for training and once for testing. The two well-known cross-validation methods are the k-fold and the leave-one-out. The system was tested using the 4-fold cross-validation method. This method reduces the possibility of deviations in the results due to some special distribution of training and test data, and ensures that the system is tested with different samples from those it has seen for training. By using this method, four accuracies are obtained from the four test sets so that it is possible to compute the average accuracy. The classification results of the EEG signals under two emotional stress states is given in Table 4.
Emotional stress classification accuracy on EEG signals using SVM for the three-kernel function
Table 5 gives the average classification accuracy in different five channels of EEG signals under two emotional stress states with using RBF kernel in SVM and ENN classifiers.
The average classification accuracy in different channels using RBF kernel of SVM and ENN classifiers
Table 6 gives the average classification accuracy in different features set under two emotional stress states with using RBF kernel in SVM and ENN classifiers.
The average classification accuracy in different features set using RBF kernel of SVM and ENN classifiers
In this research, we propose an approach to classify emotional stress in the two main areas of the valance-arousal space by using bio-signals. Several researchers have shown that, it is possible to measure emotional cues using EEG measurements, which is an important condition to be able to find emotional stress states from brain activity (Chanel, 2009;\n\t\t\t\tHorlings, 2008; Takahashi, 2004). We chose the picture presentation test, base on the closeness of its assessment to our aims. The reason we have chosen the brain signals over the pure peripheral signals is the fact that brain signals represent behaviour directly from their source but the peripheral signals are secondary manifestations of the autonomic nervous system in response to emotional stress.
With compare to the results analysis of peripheral signals, we will notice that the breathing and SC signals are less reliable in accuracy compared to BVP and HRV signals. The results showed that, the classification accuracy with peripheral signals was 76.95% for the two categories, using SVM classifier with RBF kernel. In order to choose the best channels for EEG signals, we implemented a new cognitive model (Hosseini et al., 2010a) and eventually used signals from frontal, temporal and occipital electrodes as the most important ones. The mere use of the personal moods and the subject’s self-assessment to confirm the quality of the registered brain signals can cause many errors. As a result, we will need to use peripheral signals as a secondary trainer. In order to choose the best emotional stress state correlated EEG signals, we implemented a new emotion-related signal recognition system, which has not been studied so far (Hosseini & Khalilzadeh, 2010). We recorded peripheral signals concomitantly in order to firstly recognize the correlated emotional stress state and then label the correlated EEG signal. Recent researches on the EEG signals, revealed the chaotic nature of this signal. It is logical not to use conventional methods that assume emotion can be analyzed by linear models, because brain signals essentially have a chaotic nonlinear behaviour, we performed emotional stress state assessment using both linear and nonlinear features. Wavelet coefficients, higher order spectra and chaotic invariants like fractal dimension, approximate entropy and correlation dimension were used to extract the characteristics of the EEG signals. For most nonlinear measures a dimension should be defined to visualize the attractor in phase space, but the problem associated with all of them is that defined dimension for the phase space is not constant for all channels of recorded EEG signals or for different subjects, and depending on the conditions, the chosen dimension can be different. On the other hand, the performance of each measure can be dependent to the values of dimension, so by helping some equations and trial and error the optimum dimension for getting the best results can be discovered.
The results showed that, the correlation dimension of negative emotional stress state is less as compared to that of calm state, and be observed that Higuchi’s algorithm indicates similar trend of reduction in FD value for negative emotional stress state compared to calm state. The reduction in FD values and D2 characterizes the reduction in brain system complexity for participants with negative emotional stress state, therefore the number of the necessary dynamic equations for the description of the brain state in the negative emotional stress state decreases. A new approach to emotional stress states analysis by approximate entropy is described in this research. Approximate entropy is defined as a quantitative parameter to character the complexity (or irregularity) of EEG signals in different brain function status. The results of analysis of the nonlinear characteristics show that, if the parameters and the length of data are determined appropriately, the results can be a good representation of the brain behaviour in emotional stress states. Hence, the application of nonlinear time series analysis to EEG signals offers insight into the dynamical nature and variability of the brain signals. Therefore, those seem that nonlinear features would lead to better understanding of how emotional activities work.
In this research, for the first time in this investigational field, we had done a feature extraction using higher order spectra in emotional stress states assessment. The review of the contour plots in different channels of EEG signals as examples for in Figs. 10 and 11. This figures show that, most of the changes are amplification or diminish of the peaks or transfer of the peaks in the bifrequency plane. We concluded that HOS analysis could be an accurate tool in assessment of emotional stress states.
In this research, two of the advantages in this research, which confirm the credibility of our results, are using dichotic hearing test and using peripheral signals to label the brain signals. We have used both a static and a dynamic classifiers. The results show that, no meaningful different is not seen. Therefore, we can deduce that in short term data acquisition there is no specific dynamicity, which can be attributed to the short time intervals of 2 seconds. It is possible that by performing longer tests and using bigger intervals there is hope to identify some dynamics.
The results showed that, the importance of EEG signals for emotional stress assessment by classification as they have better time response than peripheral signals. We used 2 seconds time intervals with rectangular window without overlap, to analyze the brain signals, which resulted in a time resolution of 2 seconds in emotional stress states recognition. If we had used shorter time intervals with overlap, we could have achieved a greater but virtual time resolution, which, for example, can be useful in biofeedback applications. The problem of high dimensionality is solved by using Genetic Algorithm as a feature selection method. The results showed that, the average classification accuracy were 84.6% and 83.1% for two categories of emotional stress states using the SVM and ENN classifiers respectively. Therefore, each of two classifiers are same results in recognize of emotional stress state. In addition, the results showed that, this new fusion link, between EEG and peripheral signals are more robust in comparison to the separate signals. This is a great improvement in results compared to other similar published researches. We achieved a noticeable improvement of 6.3% and 10% in accuracy, using SVM and ENN classifiers respectively, in compared to our previous studies in the similar field (Hosseini et al., 2009).
Analyzing the results of previous researches is a difficult task, because to compare the results of the researches, which attempt to introduce emotion assessment systems as a classification problem, it is important to consider the way that emotions are elicited and the number of participants, the latter is important especially to introduce a user independent system. Due to these differences, we cannot exactly compare results with the results of the researches.
Bone grafting is a surgical procedure that has been used for many years, especially in the fields of orthopedics, neurosurgery, and plastic surgery. Bone grafts are used for filling cystic defects, for bone fractures and arthrodesis treatment, and also for traumatic bone defects or loss of bone lesions that occur after removal of the tumor. It has been reported that allograft use increases in revisions of arthroplasty and in vertebral fusions in the last 10 years [1].
\nBone grafting is needed and used in orthopedic surgery and plastic surgery for the fracture repair and skeletal reconstruction of the craniofacial region during the first century. Although the first recipe, up to the seventeenth century, continued to improve day by day in terms of better understanding of the pathophysiology of bone grafts and developing new techniques, especially for the removal of vascular bone grafts. Nonvascular bone grafts are still widely used in fracture repair and reconstruction, along with new developments in bone morphogenetic protein and stem cell-supporting areas, leading to more favorable use of vascular bone transfers due to benefits such as good osteogenic properties, resistance to infection and over time hypertrophy.
\nThe graft is a dead structure, providing new bone formation and replacing the new bone implant. While grafts are rapidly incorporated into the body, others integrate differently. Some of the grafts cause inflammation and are rejected. Some grafts are completely inert. However, successful results are usually obtained with bone grafts.
\nBone and periosteum were expressed at the beginning of the nineteenth century with biological potential. Reliable and pioneering clinical applications began with the reconstruction of a diaphysis of a child’s arm-bone by Macewen in 1881 [2].
\nAutogenous cancellous bone graft is now considered the “gold standard” for fracture healing, to fill spine fusions and bone defects. The main reason for this is the essential components [osteoprogenitor cells, osteoconductive hydroxyapatite collagen matrix and bone matrix protein (BMP)] that help bone healing [3].
\nGrafts are used as a skeleton to provide bone formation and support wound healing. The grafts also act as a mineral reservoir to aid in the formation of new bone. Bone grafting is a surgical procedure. It aims to replace missing bones with the artificial or natural substitute material of the patient’s own body. As the natural bone grows, it usually replaces the graft material completely and results in a completely new bone area [4].
\nDifferent sources and origins and various bone graft categories and graft replacements are available. Despite the availability of a wide variety of options, the availability of autologous bone grafts may be limited. In addition, the procedure for collecting material is associated with many complications [5].
\nBone grafting has been used to stimulate the healing process over a period of 300 years [6], but the mechanisms by which research has emerged over the last 30 years have been uncovered.
\nFresh autogenous bone graft stimulates osteogenesis by three main mechanisms [7]. The first is the direct addition of osteogenic progenitor cells to the local population. These cells are necessary for the differentiation of new bone forming cells. Secondly, autogenous bone grafting is a structural cage for the attachment of osteoblasts, and osteoconductivity for matrix support for supporting cells. The third mechanism is osteoinduction. It refers to the ability of the bone graft to pick up the surrounding mesenchymal cells and direct their differentiation to bone and cartilage forming cells.
\nAutografts contain a large number of bone growth factors that stimulate the growth of new blood vessels into the graft and encourage migration of bone-forming cells to the injury site. Bone grafts also function as structural support.
\nThe healing of the bone graft and healing occurs with the same healing phases such as induction, inflammation, soft callus, hard callus and remodeling. The mechanical conditions around the fractured have a great effect on the morphology. According to Wolff’s law, compression-electro-negative areas during bone remodeling increase bone formation. Tension-electropositive areas increase bone resorption [8]. Mechanical stabilization is very important in the healing of the defect [9].
\nAutogenous bone grafting can cause significant donor site morbidity if large structural parts are required [10]. Permission to receive grafts in limited quantities of the donor site, variable graft quality, increased the duration of anesthesia, blood loss and cost are important postoperative complications. For these reasons, surgeons have searched for other options for grafting in the management of bowel and skeletal defects.
\nBone grafts that are used effectively in the treatment of bone defects are named differently according to the source: autograft, allograft and xenograft.
\nAutograft is the transfer of a piece of tissue in the same individual without the veins that will continue to bleed from one place to another. Autogenous bone grafts; osteogenic, osteoinductive and osteoconductive capacities. It also forms a living cell source that is not immunologically rejected. Autografts are better than allografts and xenografts [11].
\nAutografts are preferred, especially where osteogenesis is the primary goal, because in autografts, “creeping substitution” develops much faster. In addition, autografts contain osteoblasts, bone marrow and blood cells, osteogenetic induction capacities and osteogenesis contributing bunions. Other types of grafts cannot contribute to osteogenesis because they stimulate the immune response [12].
\nThere is an important distinction in autografts in terms of osteogenetic activity. In spongious built and compact autografts, “creeping substitution” develops in completely different form and speed. The open and hollow structure of the spongious bone allows for easier diffusion of newly formed vessels during the revascularization phase. Microanastomoses are easier to establish and blood supply to the graft is provided early. However, a more compact bone graft creates a barrier to vascular invasion. Vascular penetration occurs only through Haversian channels. In addition, osteogenesis and callus formation is easier because the large area surface area of the spongious bone contains many more osteoprogenitor cells [13].
\nThe region where autografts are taken also has an importance in terms of osteogenesis. For example, osteoprogenitor cells, such as grafts, iliac crest bone grafts, primitive reticulocytes, immature hematopoietic cells, integrate rapidly into the bone to which they are implanted. In order for all the above rules to be valid, the autograft should first be well established [12].
\nDuring the well-established grafting, it is first observed that the new vessels of the grafted microcavities have formed a mesenchymal stem cell pool in the graft. These cells have the capacity to differentiate into osteogenic, chondrogenic or even fibrogenic cell lines.
\nThe direction of this differentiation determines local, nutritional and electromechanical forces. For example, high oxygenation and compression allow mesenchymal stem cells to develop in the direction of osteoblast. Low oxygenation and compression lead to chondroblast, high oxygenation and tensile forces lead to fibroblast growth [14].
\nDespite these features of autografts, they can exhibit up to 50% resorption, sequestration and inadequate integration [15]. Development of alternatives to bone autografts due to limited donor sites and potential donor-acquired morbidity has been a constant focus [16]. The success of bone grafts is also due to the presence of osteocompetent cells in the graft, the availability of the recipient site and the lack of immunological response [17].
\nAfter transplantation of bone autograft, a number of basic histologic events take place in the recipient area [18]. After transplantation, the graft is covered with hematoma, inflammatory events occur in which the inflammatory cells reach the site, and then new blood vessel formation takes place. Nonvascularized autografts turn into necrosis over time. Most of the osteocytes in the graft die, but those with superficial settling can survive [19]. The blood vessels originating from the recipient site proliferate into the remaining graft tissue and the recipient osteocytes mesenchymal stem cells multiply in the graft. Vascular growth occurs from the Haversian channels present in the graft. Initially, osteoclastic resorption activity is increased, resulting in reduced graft porosity and durability. Cancellous bone is revascularized in a short period of 2–3 days due to open structure. Conversely, revascularization of the cortical bone may last up to 2 months. The fact that vascular touch is invasive into the graft, bringing osteoblasts to the site and osteoblasts’ new bone production is a “creeping substitution” phenomenon that occurs in normal fracture physiology [20]. Resorption of necrotic bones in the cortical bone graft is incomplete and therefore the final live and dead bone-mixed touch cannot reach the cancellous bone [21].
\nAutografts; cancellous, nonvascularized cortical, vascularized cortical and bone marrow. Different grades have osteogenic, osteoconductive and osteoinductive properties.
\nCortical bone grafts are less successful as biocompatibility than autogenous cancellous bone grafts. Due to the low porosity of the cortical bone, it is difficult and slow to move the vascular structures into the graft. The cortical bone contains fewer osteoblastic progenitor cells than the trabecular bone. The cells in the cortical bone are less resistant to transplantation because of the diffuse oxygen and less nutrient transfer [6].
\nCortical bone grafts are a good choice for repair of segmental bone defects smaller than 5–6 cm. Fibula, costa and iliac crest can be used as cortical bone autograft. Osteoprogenitor is poor in cells, osteoconductive and osteoinductive properties are low. They provide strong structural support. The recipient is joined with a creeping substitution process on the tissue. The recipient is fed with plasmatic imbibition from the capillary structures in the wound bed. Complete revascularization does not occur before 1–2 months. This time it is two times that of the cancellous bone. Cortical autografts are less revascularized and less remodeled than spongious autografts. Autologous cortical bone grafts are good choice for bone loss above 5–6 cm. However, vascular grafts are preferred because of the 25–50% failure rate of nonvascular grafts over 12 cm in bone loss [22]. Cortical porosity for revascularization and repair is one of the most important reasons for the occurrence of graft fracture, delayed union or nonunion, especially in large cortical grafts. Cortical grafts initially have structural durability. But between 6 and 18 months of age, about one-third of your power is lost in the stages of re-vascularization and restructuring. Over time, it approaches normal structure and reaches to the power of normal cortical bone in about 2 years. Nonetheless, nonviable bone islands in the graft continue to survive [23].
\nAutologous cancellous bone grafts are currently known as the most effective graft material for spinal fusion, filling bone defects and bone healing in fracture treatment. Osteogenic bone and bone marrow cells, osteoconductive collagen and mineral matrix, matrix proteins and osteoinductive matrix proteins are transplanted into autogenous cancellous bone. It has been shown that primitive osteogenic cells survive posttransplantation and transform into osteoblasts in the new bone tissue developed after autograft application [24]. Although the cancellous bone is known to be osteoinductive, there is no evidence that inductive proteins and cytokines are active in autologous cancellous grafts [22].
\nCancellous bones are fast revascularized grafts. Surface osteoblasts and endosteal cells can transplicate. Creeping acts as an osteoconductive substrate that effectively supports substitution. The cancellous graft cannot provide the acute structural support provided by the cortical graft. Although cancellous autografts do not initially have carrier-carrying properties, bone grafting builds upon the graft and structural integrity of the bone with the recipient bones begins to form. As bone mass increases, endurance increases and the resulting new texture is restructured in the direction of Wolf’s rules. However, it can be strengthened as fast as cortical graft for 6–12 months. Among the sources of cancerous bone, the posterior iliac crest comes first. The most frequent site of grafting is iliac crest. Major complications were reported as 8.6% and minor complications as 20.6% [25]. Other sources of grafts are Gerdy’s tubercle, distal radius and distal tibia [22].
\nWhile endochondral bone is originated from cartilage, membranous bone is originated from mesenchymal tissue. While craniofacial skeleton is formed by membranous ossification, most of the axial skeleton is formed by endochondral ossification. In graft viability, the interaction between the local mechanical environment and the cortical or cancellous nature of the bone, rather than the embryonic origin of the bone, has been shown to be important [26]. Membranous bone is generally preferred when grafting to the endochondral bone.
\nWith the progress of microsurgical techniques, autografts are frequently used in vasculature. When both artery and vein are anastomosed during transplantation, approximately 90% of the osteocytes survive and there is no osteoclastic resorption of the bone for incorporation and boiling. Areas where grafts can be removed: fibula, costa, tibia, olecranon and iliac wing. The most preferred graft is fibula graft [27]. McKee [28] reported the first microvascular anastomotic bone graft transfer at a meeting. Then the first free bone grafting articles were published [29]. In 1977, the first bone skin free flap was published [30]. Although bone grafting with vascular anastomosis using microsurgical techniques is described in the literature, “free bone graft” is used, although “free vascular bone graft” has been used in the terminology [31].
\nResorption and subsequent osteoconduction and remodeling are not observed as in nonvascular autografts and therefore are more resistant to the first 6-week period than nonvascular autografts. Osteogenic cells in vascularized bone grafts undergo less resorption. More cells live in grafts than nonvascularized grafts [32]. They do not need a good vascular space where they are transported. Vascularized bone grafts have been shown to be biomechanically superior [33].
\nVascularized bone graft acts as if there is a simple fracture on the contact surfaces of the field. Bone healing in the fracture line “Creeping substitution” occurs here. These grafts can maintain 60 min of life at room temperature. There are publications that show that reperfusion grafts will partially return in up to 6 h of ischemia [34].
\nIt is the gold standard in bone free skins because it can provide bone mass, pedicle length and skin pedicle. Both endosteal and periosteal circulation are provided. Bone healing is very good. It is the type of flap that should be considered when the receiving bed is not very good due to radiotherapy, infection and scar. Disadvantages include the possibility of damage to the peroneal nerve and tibial arteries.
\nThe ileum may be transferred as a deaf circumflex iliac artery based bone autograft or as a superficial gluteal artery based bone autograft. With this autograft, up to 4–10 cm of bone can be carried. They are used in mandibula and sub-articular reconstructions. Continuation of endosteal and periosteal circulation, donor site morbidity is low and long pedicle is a very useful flap. Hypoesthesia due to the injury of the lateral femoral cutaneous nerve and the risk of herniation are disadvantages. Posterior iliac crest is a potential source of bone used when there is segmental bone loss, such as radiotherapy, trauma and tumor resection. It is also used in diaphyseal pseudoarthrosis treatment of long bones. The bone segment that can be transferred is limited to 4–6 cm.
\nFlaps can be prepared via endosteal or periosteal pedicles. It limits the use of the vascular pedicle short.
\nThe lateral part of the scapula can be transferred so that the circumflex scapular artery is fed from musculoperiostal branches. Scapula autograft can be preferred for mandibular, orbital and maxilla reconstructions.
\nVascularized calvarial autograft can be transferred with the scalp. Vascularized transfer of calvarial bone is especially preferred for craniofacial reconstructions. Bone quality is very good. The risk of intracranial injury is the disadvantage.
\nThis graft is commonly used for the first finger of the foot. It is also applied in the form of composite tissue transfer of the first and second fingers of the foot.
\nThe radial artery gives periosteal and skin perforator branches. These vessels are fed with radial side forearm bone skin blade. A 6–12 cm of the radial bone may be included [35].
\nVascularized bone grafts are widely used in the repair of many carpal pathologies. Many pedicle grafts such as radial volar, radial dorsal and second metacarpal have been described [36].
\nThe flap was first populated by Sakai [37]. Eighty per cent of the cases of the major pedicle of the flap are descending genic artery with medial branch of superficial femoral artery. The corticoperiosteal bone graft can be removed from the medial femoral condyle up to 8 × 13 cm2. It is a source of grafts for the repair of relatively small bone defects.
\nIt is a very good resource for cortical and cancellous bones. Easy to reach, can be taken in any amount. The disadvantage is that the donor area is also postoperative pain. Anterior grafts can be taken from superior iliac spine and crest tubercle. No damage to the tendinous ligaments is important to prevent the possibility of gait disturbance. The lateral femoral cutaneous nerve may be damaged during graft retrieval. Paresthesia on the outside of the leg results in hypoesthesia. If large segments are taken from ileum, herniation due to inguinal ligament integrity may occur.
\nCalvarial bone split grafts are used. In an adult patient, the average thickness of the calvarium is assumed to be 7 mm. The thickest part of the bone is the parietal area behind the coronal suture. Calcium is not preferred in children and adolescents because of the risk of dura and brain damage during graft ingestion.
\nCosta grafts are used for mandibular or craniomaxillary zone reconstructions. Unlike whole-layer costa grafts, partial thickness jeans grafts provide cortical bone with a larger surface area. The regeneration ability of the casts allows the partial autogenous cortical graft to be taken several times over the same donor site.
\nCortical and cancellous bone needs are not among the preferred donor sites. They are preferred in situations where the iliac crest is unavailable.
\nBone marrow can be used alone as an osteogenic graft. Bone marrow obtained after aspiration; cytokines, osteoblastic progenitors such as other bone marrow-derived cells, and a rapidly revascularized absorbable biological fibrin matrix. An average of 1400 connective tissue progenitors was found in the iliac crest aspirated bone marrow [38]. Bone marrow should be used immediately after aspiration.
\nBone transfers are genetic characteristics made between different individuals. The first literature report was made by MacKewen [39]. Allografts; porous structures contain many chemical domains that are retained by progenitor cells and endothelial cells. They also contain growth factors in the bone matrix that are released when resorbed by osteoclasts. The allograft bone also contains a small amount of bone morphogenic protein with osteoinductive properties.
\nDemineralization increases the bioavailability of growth factors in the allograft bone matrix. In addition, demineralization prevents HIV infection [40].
\nIn the early allograft use, the graft cells were completely destroyed and the skeleton of the bone roof served as a scaffold. Fresh bone allografts result in both humoral and cellular immunological responses, which allow the graft to be recognized by the recipient. Antibody production results in cell lysis and vascular destruction resulting in graft rejection. The frequency of allograft rejection depends on the degree of antigen mismatch between the graft and the recipient. Vascularized bone allograft rejection is seen on postoperative third day. The first affected members have been shown to be osteocytes and vascular endothelium [41]. Rejection can be suppressed by the use of cyclosporine. Every allograft causes an immunological reaction in the recipient.
\nThe alignment of allografts is different than that of autografts. Both vascular invasion and perivascular new bone formation are slower. This adaptation also affects the size of the graft, the level of immunological response to the graft and the conditions under which allograft is stored [12].
\nThe immunological response to allografts results in the sensitization of the recipient to histocompatibility antigens in osteogenic and hematopoietic cells, leukocytes, blood vessels, nerves and connective tissue matrices within the graft. Therefore, this is a secondary immunological response. This is a cellular immune response [42]. Herndon et al. allografts have found widespread use, demonstrating that the immune response with frozen allografts decreases. Attention has shifted to allograft preservation techniques [43].
\nIf allografts are separated from their cells, they are prevented by immunological reactions. The bones obtained from cadavers are used as osteoconductive skeleton by decellularization [19]. Thus, the disease is prevented from passing between people. Among the processes used are irradiation, debridements, ultrasonic washing, liquid nitrogen, ethylene oxide and deep freezing. Allografts are prepared and maintained in tissue banks.
\nFrozen-dried bones are poorly immunogenic to both the humoral and cellular immune system. However, passing the bone through these processes destroys osteoinductivity when changing mechanical properties [44]. Bone banks were needed for the use and development of these methods and bone banks were established in many parts of the world.
\nThe preferred age range for choosing donors for bone banks is from 16 to 65 [45]. Donors; acute or fatal chronic infection, malignancy, exposure to radiation in the area to be caught, venereal disease, hepatitis, slow virus diseases, AIDS or HIV infection, drug use, steroid use for more than 1 week, diffuse osteoporosis, immune complex disease, connective tissue disorders and long-term insulin-dependent diabetes should not have an anamnesis such as grafting with live virus vaccine in the near future [46, 47]. Donors can live in cadaver. Live donors require adequate physical examination and a good anamnesis, and a detailed autopsy is required for cadavers.
\nDemineralized bone matrix (DBM) is used to fill bone defects and voids as an osteoconductive and osteoinductive material. DBM is rapidly re-vascularized and is also a good carrier for autologous bone marrow. There are differences between tissue banks and firms according to DBM acquisition phases [22]. Studies have shown that DBM results in long bone pseudoarthroses and bone loss similar to autologous bone grafts [48]. DBM can be used by mixing with cancellous graft to increase and intensify autologous bone graft when bone loss is large. It can also be considered as an alternative in patients who cannot use autologous bone graft [22].
\nThey are osteoconductive. They provide mechanical support against compression. They are prepared by freeze-drying (lyophilization) and vacuum packaging. It can be used to fill cavities formed after curettage in bone cysts and to remove bone surfaces in periarticular metaphyseal fractures.
\nThey are obtained from the pelvis, costume, femur, tibia and fibula and used in major bone and joint loss. They also provide both structural and mechanical support for the treatment of periprosthetic fractures. They carry osteoconductive properties.
\nAnother species is the use of the bones of living things. Bones from various animal species have been tested since allograft is an expensive method of providing. However, they were abandoned because of their high immunity, insufficient biomechanical qualities and foreign body reaction [49].
\nDog tibia, calvarial bone transplants have been reported for the defect in human bones [49]. But it has been understood that it is not useful in the human body [50]. Cell-free and demineralized xenografts have been used but have been shown to destroy bone morphogenic proteins and other growth factors [51].
\nBone grafting is the most commonly used method for increasing bone regeneration in surgical procedures [52]. More than 2 million bone grafting procedures have been performed worldwide every year. Immediately after blood transfusions, it is the second most common tissue transplant [52]. Important when deciding on the use of bone grafts; patient factors, environmental factors, the experience of the surgeon and the economic dimension of graft use.
\nConditions limiting the use of autografts and allografts have accelerated the development of bioceramic technology. As the future of bone grafting procedures, new technologies will emerge in the isolation and production of recombinant human bone morphogenic proteins and growth factors and in the application of autogenous stem cells.
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
\n\nHowever, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
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\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"105746",title:"Dr.",name:"A.W.M.M.",middleName:null,surname:"Koopman-van Gemert",slug:"a.w.m.m.-koopman-van-gemert",fullName:"A.W.M.M. Koopman-van Gemert",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/105746/images/5803_n.jpg",biography:"Dr. Anna Wilhelmina Margaretha Maria Koopman-van Gemert MD, PhD, became anaesthesiologist-intensivist from the Radboud University Nijmegen (the Netherlands) in 1987. She worked for a couple of years also as a blood bank director in Nijmegen and introduced in the Netherlands the Cell Saver and blood transfusion alternatives. She performed research in perioperative autotransfusion and obtained the degree of PhD in 1993 publishing Peri-operative autotransfusion by means of a blood cell separator.\nBlood transfusion had her special interest being the president of the Haemovigilance Chamber TRIP and performing several tasks in local and national blood bank and anticoagulant-blood transfusion guidelines committees. Currently, she is working as an associate professor and up till recently was the dean at the Albert Schweitzer Hospital Dordrecht. She performed (inter)national tasks as vice-president of the Concilium Anaesthesia and related committees. \nShe performed research in several fields, with over 100 publications in (inter)national journals and numerous papers on scientific conferences. \nShe received several awards and is a member of Honour of the Dutch Society of Anaesthesia.",institutionString:null,institution:{name:"Albert Schweitzer Hospital",country:{name:"Gabon"}}},{id:"83089",title:"Prof.",name:"Aaron",middleName:null,surname:"Ojule",slug:"aaron-ojule",fullName:"Aaron Ojule",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Port Harcourt",country:{name:"Nigeria"}}},{id:"295748",title:"Mr.",name:"Abayomi",middleName:null,surname:"Modupe",slug:"abayomi-modupe",fullName:"Abayomi Modupe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:null,institutionString:null,institution:{name:"Landmark University",country:{name:"Nigeria"}}},{id:"94191",title:"Prof.",name:"Abbas",middleName:null,surname:"Moustafa",slug:"abbas-moustafa",fullName:"Abbas Moustafa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94191/images/96_n.jpg",biography:"Prof. Moustafa got his doctoral degree in earthquake engineering and structural safety from Indian Institute of Science in 2002. He is currently an associate professor at Department of Civil Engineering, Minia University, Egypt and the chairman of Department of Civil Engineering, High Institute of Engineering and Technology, Giza, Egypt. He is also a consultant engineer and head of structural group at Hamza Associates, Giza, Egypt. Dr. Moustafa was a senior research associate at Vanderbilt University and a JSPS fellow at Kyoto and Nagasaki Universities. He has more than 40 research papers published in international journals and conferences. He acts as an editorial board member and a reviewer for several regional and international journals. His research interest includes earthquake engineering, seismic design, nonlinear dynamics, random vibration, structural reliability, structural health monitoring and uncertainty modeling.",institutionString:null,institution:{name:"Minia University",country:{name:"Egypt"}}},{id:"84562",title:"Dr.",name:"Abbyssinia",middleName:null,surname:"Mushunje",slug:"abbyssinia-mushunje",fullName:"Abbyssinia Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Fort Hare",country:{name:"South Africa"}}},{id:"202206",title:"Associate Prof.",name:"Abd Elmoniem",middleName:"Ahmed",surname:"Elzain",slug:"abd-elmoniem-elzain",fullName:"Abd Elmoniem Elzain",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Kassala University",country:{name:"Sudan"}}},{id:"98127",title:"Dr.",name:"Abdallah",middleName:null,surname:"Handoura",slug:"abdallah-handoura",fullName:"Abdallah Handoura",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Supérieure des Télécommunications",country:{name:"Morocco"}}},{id:"91404",title:"Prof.",name:"Abdecharif",middleName:null,surname:"Boumaza",slug:"abdecharif-boumaza",fullName:"Abdecharif Boumaza",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Abbès Laghrour University of Khenchela",country:{name:"Algeria"}}},{id:"105795",title:"Prof.",name:"Abdel Ghani",middleName:null,surname:"Aissaoui",slug:"abdel-ghani-aissaoui",fullName:"Abdel Ghani Aissaoui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/105795/images/system/105795.jpeg",biography:"Abdel Ghani AISSAOUI is a Full Professor of electrical engineering at University of Bechar (ALGERIA). He was born in 1969 in Naama, Algeria. He received his BS degree in 1993, the MS degree in 1997, the PhD degree in 2007 from the Electrical Engineering Institute of Djilali Liabes University of Sidi Bel Abbes (ALGERIA). He is an active member of IRECOM (Interaction Réseaux Electriques - COnvertisseurs Machines) Laboratory and IEEE senior member. He is an editor member for many international journals (IJET, RSE, MER, IJECE, etc.), he serves as a reviewer in international journals (IJAC, ECPS, COMPEL, etc.). He serves as member in technical committee (TPC) and reviewer in international conferences (CHUSER 2011, SHUSER 2012, PECON 2012, SAI 2013, SCSE2013, SDM2014, SEB2014, PEMC2014, PEAM2014, SEB (2014, 2015), ICRERA (2015, 2016, 2017, 2018,-2019), etc.). His current research interest includes power electronics, control of electrical machines, artificial intelligence and Renewable energies.",institutionString:"University of Béchar",institution:{name:"University of Béchar",country:{name:"Algeria"}}},{id:"99749",title:"Dr.",name:"Abdel Hafid",middleName:null,surname:"Essadki",slug:"abdel-hafid-essadki",fullName:"Abdel Hafid Essadki",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Nationale Supérieure de Technologie",country:{name:"Algeria"}}},{id:"101208",title:"Prof.",name:"Abdel Karim",middleName:"Mohamad",surname:"El Hemaly",slug:"abdel-karim-el-hemaly",fullName:"Abdel Karim El Hemaly",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/101208/images/733_n.jpg",biography:"OBGYN.net Editorial Advisor Urogynecology.\nAbdel Karim M. A. El-Hemaly, MRCOG, FRCS � Egypt.\n \nAbdel Karim M. A. El-Hemaly\nProfessor OB/GYN & Urogynecology\nFaculty of medicine, Al-Azhar University \nPersonal Information: \nMarried with two children\nWife: Professor Laila A. Moussa MD.\nSons: Mohamad A. M. El-Hemaly Jr. MD. Died March 25-2007\nMostafa A. M. El-Hemaly, Computer Scientist working at Microsoft Seatle, USA. \nQualifications: \n1.\tM.B.-Bch Cairo Univ. June 1963. \n2.\tDiploma Ob./Gyn. Cairo Univ. April 1966. \n3.\tDiploma Surgery Cairo Univ. Oct. 1966. \n4.\tMRCOG London Feb. 1975. \n5.\tF.R.C.S. Glasgow June 1976. \n6.\tPopulation Study Johns Hopkins 1981. \n7.\tGyn. Oncology Johns Hopkins 1983. \n8.\tAdvanced Laparoscopic Surgery, with Prof. Paulson, Alexandria, Virginia USA 1993. \nSocieties & Associations: \n1.\t Member of the Royal College of Ob./Gyn. London. \n2.\tFellow of the Royal College of Surgeons Glasgow UK. \n3.\tMember of the advisory board on urogyn. FIGO. \n4.\tMember of the New York Academy of Sciences. \n5.\tMember of the American Association for the Advancement of Science. \n6.\tFeatured in �Who is Who in the World� from the 16th edition to the 20th edition. \n7.\tFeatured in �Who is Who in Science and Engineering� in the 7th edition. \n8.\tMember of the Egyptian Fertility & Sterility Society. \n9.\tMember of the Egyptian Society of Ob./Gyn. \n10.\tMember of the Egyptian Society of Urogyn. \n\nScientific Publications & Communications:\n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Asim Kurjak, Ahmad G. Serour, Laila A. S. Mousa, Amr M. Zaied, Khalid Z. El Sheikha. \nImaging the Internal Urethral Sphincter and the Vagina in Normal Women and Women Suffering from Stress Urinary Incontinence and Vaginal Prolapse. Gynaecologia Et Perinatologia, Vol18, No 4; 169-286 October-December 2009.\n2- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nFecal Incontinence, A Novel Concept: The Role of the internal Anal sphincter (IAS) in defecation and fecal incontinence. Gynaecologia Et Perinatologia, Vol19, No 2; 79-85 April -June 2010.\n3- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nSurgical Treatment of Stress Urinary Incontinence, Fecal Incontinence and Vaginal Prolapse By A Novel Operation \n"Urethro-Ano-Vaginoplasty"\n Gynaecologia Et Perinatologia, Vol19, No 3; 129-188 July-September 2010.\n4- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n5- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n6- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n7-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n8-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n9-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n10-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n11-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n12- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n13-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n14- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Mohamad A. Rizk and Mohamad A.K.M.El Hemaly.\n Urethro-plasty, a Novel Operation based on a New Concept, for the Treatment of Stress Urinary Incontinence, S.U.I., Detrusor Instability, D.I., and Mixed-type of Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/urethro-plasty_01\n\n15-Ibrahim M. Kandil, Abdel Karim M. El Hemaly, Mohamad M. Radwan: Ultrasonic Assessment of the Internal Urethral Sphincter in Stress Urinary Incontinence. The Internet Journal of Gynecology and Obstetrics. 2003. Volume 2 Number 1. \n\n\n16-Abdel Karim M. El Hemaly. Nocturnal Enureses: A Novel Concept on its pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecolgy/?page=articles/nocturnal_enuresis\n\n17- Abdel Karim M. El Hemaly. Nocturnal Enureses: An Update on the pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecology/?page=/ENHLIDH/PUBD/FEATURES/\nPresentations/ Nocturnal_Enuresis/nocturnal_enuresis\n\n18-Maternal Mortality in Egypt, a cry for help and attention. The Second International Conference of the African Society of Organization & Gestosis, 1998, 3rd Annual International Conference of Ob/Gyn Department � Sohag Faculty of Medicine University. Feb. 11-13. Luxor, Egypt. \n19-Postmenopausal Osteprosis. The 2nd annual conference of Health Insurance Organization on Family Planning and its role in primary health care. Zagaziz, Egypt, February 26-27, 1997, Center of Complementary Services for Maternity and childhood care. \n20-Laparoscopic Assisted vaginal hysterectomy. 10th International Annual Congress Modern Trends in Reproductive Techniques 23-24 March 1995. Alexandria, Egypt. \n21-Immunological Studies in Pre-eclamptic Toxaemia. Proceedings of 10th Annual Ain Shams Medical Congress. Cairo, Egypt, March 6-10, 1987. \n22-Socio-demographic factorse affecting acceptability of the long-acting contraceptive injections in a rural Egyptian community. Journal of Biosocial Science 29:305, 1987. \n23-Plasma fibronectin levels hypertension during pregnancy. The Journal of the Egypt. Soc. of Ob./Gyn. 13:1, 17-21, Jan. 1987. \n24-Effect of smoking on pregnancy. Journal of Egypt. Soc. of Ob./Gyn. 12:3, 111-121, Sept 1986. \n25-Socio-demographic aspects of nausea and vomiting in early pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 35-42, Sept. 1986. \n26-Effect of intrapartum oxygen inhalation on maternofetal blood gases and pH. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 57-64, Sept. 1986. \n27-The effect of severe pre-eclampsia on serum transaminases. The Egypt. J. Med. Sci. 7(2): 479-485, 1986. \n28-A study of placental immunoreceptors in pre-eclampsia. The Egypt. J. Med. Sci. 7(2): 211-216, 1986. \n29-Serum human placental lactogen (hpl) in normal, toxaemic and diabetic pregnant women, during pregnancy and its relation to the outcome of pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:2, 11-23, May 1986. \n30-Pregnancy specific B1 Glycoprotein and free estriol in the serum of normal, toxaemic and diabetic pregnant women during pregnancy and after delivery. Journal of the Egypt. Soc. of Ob./Gyn. 12:1, 63-70, Jan. 1986. Also was accepted and presented at Xith World Congress of Gynecology and Obstetrics, Berlin (West), September 15-20, 1985. \n31-Pregnancy and labor in women over the age of forty years. Accepted and presented at Al-Azhar International Medical Conference, Cairo 28-31 Dec. 1985. \n32-Effect of Copper T intra-uterine device on cervico-vaginal flora. Int. J. Gynaecol. Obstet. 23:2, 153-156, April 1985. \n33-Factors affecting the occurrence of post-Caesarean section febrile morbidity. Population Sciences, 6, 139-149, 1985. \n34-Pre-eclamptic toxaemia and its relation to H.L.A. system. Population Sciences, 6, 131-139, 1985. \n35-The menstrual pattern and occurrence of pregnancy one year after discontinuation of Depo-medroxy progesterone acetate as a postpartum contraceptive. Population Sciences, 6, 105-111, 1985. \n36-The menstrual pattern and side effects of Depo-medroxy progesterone acetate as postpartum contraceptive. Population Sciences, 6, 97-105, 1985. \n37-Actinomyces in the vaginas of women with and without intrauterine contraceptive devices. Population Sciences, 6, 77-85, 1985. \n38-Comparative efficacy of ibuprofen and etamsylate in the treatment of I.U.D. menorrhagia. Population Sciences, 6, 63-77, 1985. \n39-Changes in cervical mucus copper and zinc in women using I.U.D.�s. Population Sciences, 6, 35-41, 1985. \n40-Histochemical study of the endometrium of infertile women. Egypt. J. Histol. 8(1) 63-66, 1985. \n41-Genital flora in pre- and post-menopausal women. Egypt. J. Med. Sci. 4(2), 165-172, 1983. \n42-Evaluation of the vaginal rugae and thickness in 8 different groups. Journal of the Egypt. Soc. of Ob./Gyn. 9:2, 101-114, May 1983. \n43-The effect of menopausal status and conjugated oestrogen therapy on serum cholesterol, triglycerides and electrophoretic lipoprotein patterns. Al-Azhar Medical Journal, 12:2, 113-119, April 1983. \n44-Laparoscopic ventrosuspension: A New Technique. Int. J. Gynaecol. Obstet., 20, 129-31, 1982. \n45-The laparoscope: A useful diagnostic tool in general surgery. Al-Azhar Medical Journal, 11:4, 397-401, Oct. 1982. \n46-The value of the laparoscope in the diagnosis of polycystic ovary. Al-Azhar Medical Journal, 11:2, 153-159, April 1982. \n47-An anaesthetic approach to the management of eclampsia. Ain Shams Medical Journal, accepted for publication 1981. \n48-Laparoscopy on patients with previous lower abdominal surgery. Fertility management edited by E. Osman and M. Wahba 1981. \n49-Heart diseases with pregnancy. Population Sciences, 11, 121-130, 1981. \n50-A study of the biosocial factors affecting perinatal mortality in an Egyptian maternity hospital. Population Sciences, 6, 71-90, 1981. \n51-Pregnancy Wastage. Journal of the Egypt. Soc. of Ob./Gyn. 11:3, 57-67, Sept. 1980. \n52-Analysis of maternal deaths in Egyptian maternity hospitals. Population Sciences, 1, 59-65, 1979. \nArticles published on OBGYN.net: \n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n2- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n3- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n4-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n5-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n6-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n7-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n8-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n9- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n10-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n11- Abdel Karim M. El Hemaly*, Ibrahim M. 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