Final testing and training results of ANN model (from Ref. [35]).
\r\n\tgas sensors.
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D in Physics in 2012 from Indian Institute of Technology Guwahati, India. Presently, he is associated with the Faculty of Science, Sri Sri University, India as an Assistant Professor in Physics. Prior to joining the current\naffiliation, he was a postdoctoral fellow at different renowned institutions, Kobe University Japan, S. N. Bose National Centre for Basic Sciences, India and Cardiff University, United Kingdom. He was awarded prestigious JSPS postdoctoral fellowship based on his research contribution on semiconducting nanowires. He has published more than 32 research articles including 1 review article in high profile international journals and 3 book chapters to his credit. His research trust areas of interests are semiconductor nanostructures, optoelectronics, solid state lighting and light sensors, spectroscopy of nanomaterials, thin-film transistors (TFTs) etc.",institutionString:"Sri Sri University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Sri Sri University",institutionURL:null,country:{name:"India"}}}],coeditorOne:{id:"442408",title:"Dr.",name:"Gorachand",middleName:null,surname:"Dutta",slug:"gorachand-dutta",fullName:"Gorachand Dutta",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Gorachand Dutta, PhD is an Assistant Professor with the School of MedicalScience and Technology, Indian Institute of Technology Kharagpur. His research interests include the design and characterization of portable\r\nbiosensors, biodevices and sensor interfaces for miniaturized systems and biomedical applications for point-of-care testing. He received his Ph.D in Biosensor and Electrochemistry from Pusan National University, South Korea,\r\nwhere he developed different class of electrochemical sensors and studied the electrochemical properties of gold, platinum, and palladium based metal electrodes. He completed his Post-doctoral fellowships in the Department of\r\nMechanical Engineering, Michigan State University, USA and Department of Electronic and Electrical Engineering at University of Bath, UK. He has expertise on label-free multichannel electrochemical biosensors, electronically\r\naddressable biosensor arrays, aptamer- and DNA-based sensors and surface bio-functionalization.",institutionString:"Indian Institute of Technology Kharagpur",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Indian Institute of Technology Kharagpur",institutionURL:null,country:{name:"India"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429341",firstName:"Paula",lastName:"Gavran",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"paula@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"10198",title:"Response Surface Methodology in Engineering Science",subtitle:null,isOpenForSubmission:!1,hash:"1942bec30d40572f519327ca7a6d7aae",slug:"response-surface-methodology-in-engineering-science",bookSignature:"Palanikumar Kayaroganam",coverURL:"https://cdn.intechopen.com/books/images_new/10198.jpg",editedByType:"Edited by",editors:[{id:"321730",title:"Prof.",name:"Palanikumar",surname:"Kayaroganam",slug:"palanikumar-kayaroganam",fullName:"Palanikumar Kayaroganam"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Such high-frequency circuits are leading to the development of a large variety of microwave models for passive and active devices and circuit components [1]. Modeling and computer-aided design (CAD) methods have an essential role in microwave designs and simulations [2]. The older approaches were mainly based on slow trial-and-error processes and an emphasis on performance at any price, but today seems to be a new era in high-frequency circuit design and modeling, since development in this ground has enabled microwave engineers to design larger, more efficient, and more complicated circuits than before [1, 3]. This complexity requires new materials and technologies that require not only new models but also new algorithms in computer-aided design [4] for RF/microwave circuits, antennas, and systems to keep up with the advancement of technology with emphasis on time-to-market and low-cost approaches [1, 3]. In addition to accurate parametric-modeling techniques to describe the behavior of the microwave device, a reliable description that explains the changes of its behavior against geometrical or physical parameters is also needed [5].
Also, since circuit models at high frequencies often lack fidelity, detailed electromagnetic (EM) simulation techniques are needed to improve design accuracy. Although EM simulation techniques are heavily used yet, they are computationally expensive, so there is a demand for design methodologies to be not only accurate but also fast. Another concerning problem today is optimization. To meet this purpose, computer-based algorithms that work with iterative circuit evaluation are needed; this process also needs a highly repetitive computational process. Another concerning issue according to Ref. [6] is the possibility of employing knowledge-based tools for initial design, that is, one of the steps toward designing and modeling process. It is hard to satisfy all these problems with the traditional CAD technologies [1, 3]. In conclusion, obviously there is a serious need for a powerful accurate and fast processing and modeling tool.
Neural networks (NNs), or artificial neural networks, are information-processing systems that can imitate the ability of human brain to learn from observation and generalize by abstraction to create complex models [7]. Neural network gives a great approximation of system regardless of linear or nonlinear correlation between the input data and can be used as knowledge-based tool (to be employed for initial design in RF/microwave applications) [1]. The ability of NN to be trained resulted in their use in many diverse fields such as pattern recognition, system identification, control, telecommunications, biomedical instrumentation, and many other grounds. Recently, many researchers in communication area are focusing on using neural network in their modeling and simulation, and NN has been recognized as a useful alternative to conventional approaches in microwave modeling [1, 3]. Neural network models are simple and fast, and they can enhance the accuracy of existing models. The basis of neural network is on the universal approximation theorem, which says that a neural network with at least one hidden layer can give an approximation of nonlinear multidimensional function to any intended accuracy [8]. This property makes neural network a favorite modeling tool for microwave engineers. Neural network approach is generic, that is, the same modeling technique that can be reused for passive/active devices/circuits. Another advantage of NN is the ease of updating neural models regarding changes in technology [2]. Neural network is now used in various microwave modeling and simulation applications, such as vertical interconnect accesses (Vias) and interconnects [9], parasitic modeling [10], coplanar waveguide (CWG) components [11], antenna applications, nonlinear microwave circuit optimization [12], power amplifier modeling, nonlinear device modeling, wave-guide filter, enhanced elemental method (EM) computation, and so on [2].
Artificial neural networks are classified into two main categories: static neural networks and dynamic neural networks. In this chapter, the first neural network structures will be presented, and then a general overview of static and dynamic neural networks and different types of them and their applications in microwave modeling will be discussed. The last part is devoted to another method called vector fitting (VF) that is a numerical technique used for system identification and macromodeling [13].
The idea behind neural network is similar to the function of human brain. A typical neural network structure has two types of basic components: the processing elements and interconnection between them. The processing elements are known as neurons, and the interconnections are called links. Each link is recognized with a corresponding weight parameter. Every neuron receives stimuli from neighbor neurons connected to it [3]. Input neurons receive stimuli from the outside of the network and the neurons that produce the output result are called output neurons, and neurons that not only send but also receive stimuli are called hidden neurons [1]. There are different ways to connect neurons to each other, so there are different neural network structures. A neural network structure defines how information is processed inside a neuron, and how the neurons are connected. In this chapter, we discuss the models that are more common in microwave simulations and modeling.
Generally, artificial neural networks have an input data vector, an output data vector, a vector including all the weight parameters, and a function that mathematically presents the neural network [14].
Assume
A general scheme of a neural network with four input neurons, two output neuron, and one hidden layer. Also, the links between the neurons are the weights.
Given a set of input and output data, a neural network can be constructed and trained. The network tries to estimate a function, so that it is able to give the closest result to the intended output. Commonly, a large percentage of input and corresponding output data are used as training data and the network will be trained by means of them. The act of training means identifying the weights, so that they reach the optimum values. The remaining percentage of data is used as validation and testing. Validation set is used to determine an approximation of generalized error and is a factor for determining when to stop the process to prohibit over-learning and under-learning [7]. Testing data obviously is used for checking the accuracy and correctness of the network after training is completed.
In each level of information processing, the output of each neuron is received by the next neuron, from input neurons to output neurons. An overview of information processing in layers is shown in Figure 2. The inputs of a neuron are first multiplied by the corresponding weight parameters individually, then the results are added to produce a weighted sum of
Information processing inside a neuron.
Let
There are different types of training in neural network [1], here we explain each of them shortly:
Sample-by-sample (or online) training: each time a training sample is presented, the weights (
Batch-mode (or offline) training: after each epoch weights are updated based on training error from all the samples in training data set.
Supervised training: using
Un-supervised training: using just
Activation function, also known as transfer function, is one of the most important units in a neural network structure, that is, a scalar-to-scalar function transforms a set of input signals into an output signal. Common types of activation functions are arctangent as shown in Figure 3, hyperbolic tangent shown in Figure 4, and sigmoid functions which are shown in Figure 5 [15].
Mathematical graph of arctangent function
Mathematical graph of hyperbolic tangent function
Mathematical diagram of sigmoid function
Sigmoid function:
Arctangent function:
Hyperbolic tangent function:
In the past few years, artificial neural networks have gained attention as a valuable computer-aided design tool for modeling high-frequency circuits. They can mainly be categorized as techniques for modeling frequency-domain response of components and time-domain response of them. For frequency-domain modeling, static neural networks are employed. Their main architectures are multilayer perceptron (MLP) and radial-basis function (RBF), which will be discussed in this section.
Multilayer perceptron structure is the most frequently used structure in many areas including microwave modeling and optimization problems. This technique belongs to a subcategory of neural network called feed-forward neural network, which is able to approximate continuous and integrable functions [1], and their connectivity consists of layer groups that are only linked to adjacent layers, meaning that there is not a cycle or a recursive path [16].
In MLP structure, neurons are classified into different layers. A typical MLP neural network consists of one input layer, one or more hidden layers, and one output layer, as shown in Figure 6. Consider
MLP structure with one input layer, several hidden layers, and one output layer.
Here, consider
One of the most commonly used activation functions in MLP structure is sigmoid function [1], which is shown in Figure 5.
In summary, if we suppose
For
For
And
Radial-basis function neural network, like MLP, is a subset of feed-forward neural network. It is used in a wide range of applications related to microwave transistors and high-speed integrated circuits, and modeling of intermodulation distortion behavior of MESFETs and HEMTs [1, 17].
A radial-basis function is a real-valued function whose value depends only on the distance from the origin, so that
The main approach in this structure is based on approximation of a curve that best fits to the training data set in high-dimensional space by determining
where
In RBF structure, there is just one hidden layer, and the function of input and output layers stays the same like MLP structure. RBF uses the radial basis as activation function [7]. Figure 7 shows a typical RBF neural network.
Structure of RBF neural network.
Radial-basis activation functions include Gaussian and multiquadratic functions.
Gaussian function:
Multiquadratic function:
The Gaussian and multiquadratic functions are shown in Figure 8 and Figure 9, respectively.
The Gaussian function.
The multiquadratic function.
For time-domain modeling of components and systems, time-domain artificial neural network structures are usually employed in the literature. The main time-domain architectures are dynamic neural networks (DNNs) and recurrent neural networks (RNNs), which will be discussed in this section.
Most neural network structures used by engineers are feed-forward neural networks that are suitable for time-independent static input-output mapping [18]. In feed-forward neural networks, the flow of information is straight forward from the first neuron of the first layer to the last neuron of the output layer, and the procedure is not recursive, so the output of neurons does not have any effect on the input of the last neurons, although the stability of a neural network is the result of the absence of feedback in the network. In spite of static NNs, a dynamic neural network uses feedback between neurons in the same layer, or even neurons in different layers, also it provides more computational advantages [19]. Feedback-based neural networks are good approaches for modeling, identification, and control of systems, since most of systems in real world such as airplanes, rockets, and so forth are nonlinear dynamical systems [18, 20].
Recurrent neural network is a discrete time-domain neural network that allows time-domain behaviors of a dynamic system to be modeled [1]. Its structure is suitable for modeling tasks such as dynamic system control and finite-difference time-domain (FDTD) solutions in electromagnetic modeling [21]. The output of the neural network is a function of its present inputs and a history of its inputs and outputs [22]. The delayed outputs are fed back to the inputs and the feed-forward network along with the feedback delay constructs the recurrent neural network structure. In this architecture, we suppose the inputs and outputs to be a function of time, representing this functionality with parameter
Suppose the external single input of the neural network to be
Recurrent neural network.
Suppose a three-layer discrete-time MLP neural network as above, with activation function
in which,
where
Dynamic neural network is a continuous time-domain neural network that is one of the best formulations for modeling nonlinear microwave circuits [9]. DNN is highly efficient in theory and practice. It is suitable for a wide range of needs in nonlinear microwave simulations, for example, it is suitable for both time- and frequency-domain applications, multitone simulations, and so on [12]. In comparison with other neural network methods, DNN provides a faster and more accurate network modeling that is significantly required in today’s efficient CAD algorithms in high-level and large-scale nonlinear microwave designs. DNN also can be developed directly from input-output data without a need to depend on internal details of the circuit [12]. In DNN, the outputs are a function of inputs and their derivatives, and also a function of derivatives of outputs. Figure 11 shows the architecture of dynamic neural network and the process occurring at each level.
Dynamic neural network.
In Figure 11,
The DNN model can represent a nonlinear circuit when trained and tested with an appropriate data set, measured or obtained from the original circuit.
State-space dynamic neural network (SSDNN) is a technique for modeling nonlinear transient behaviors especially in high-speed IC and nonlinear circuits. The SSDNN-modeling technique is based on DNN structure and is a combination of DNN and state-space concept, which expands continuous DNN into a more general and flexible approach for nonlinear transient modeling and design with good accuracy [23].
Let
where
Structure of SSDNN model.
Adjoint state-space dynamic neural network (ASDNN) method, like SSDNN method, is used for modeling the transient behavior of nonlinear electronic and photonic components. It is an extension of SSDNN technique that is capable of adding the derivative information of the output to the training patterns of nonlinear components simultaneously, so that the training process can be done more efficient requiring less data without sacrificing model accuracy and efficiency [23, 24]. It has been shown in Ref. [24] that testing error from the model trained by ASSDNN method is much less than that obtained from SSDNN. Here is the formulation of ASSDNN using notation similar to SSDNN mentioned already, and an overview of structure of ASSDNN is shown in Figure 13.
Structure of ASSDNN-based model.
There are other methods which are not based on neural networks for modeling microwave components such as Krylov method [25], finite element [26], mode-matching method [27], vector-fitting method, and so forth [28]. In the preceding section, we present vector-fitting method. This technique has been used in many microwave simulations and modeling researches [29–31].
Vector fitting (VF) is a robust numerical technique for rational approximation of transfer functions and
Basically, vector fitting is a pole relocation method where the poles are improved in an iterative manner. This is achieved by repeatedly solving a linear problem until convergence is achieved [13]. The VF formulation avoids the ill-conditioning problems encountered with some alternative approaches, as the formulation is given in the form of simple fractions instead of polynomials. Unstable poles are flipped into the left-half plane to enforce stable poles. This makes VF applicable to high-order systems and wide frequency bands [33].
Mathematical representation of vector-fitting method is presented briefly in this section.
Let {
in which
in which
in which
It can be proven that the poles of
Also for initialization there are different approaches. Basically, initial poles should be complex with weak attenuation and can be obtained by a simple calculation such as Prony method [13] or simply can be spaced within the desired range of frequency, for example, between 50 Hz and 1 MHz [17], and the advantage here is even if the starting poles were selected poorly, the result does not change significantly [32]. By solving Eq. (19), new set of poles are identified. After identifying all the poles, residues are calculated by solving Eq. (16) which is again a linear problem. As a conclusion, VF method samples the given function with an appropriate sample rate, and in this way a summation of partial fractions can be found, that is, the discrete-function approximation of the original transfer function.
This part of the chapter briefly discusses about the application of computer-aided design (CAD) techniques in modeling and simulation of RF and microwave-passive components. Neural network-based modeling approaches have been widely used for modeling variety of RF and microwave-passive components such as coupled-line filters, coplanar waveguides, Vias and multilayer interconnects, and some other passive components.
Here, we provide a brief review of procedure used in neural network-based modeling of RF and microwave-passive components.
For modeling microwave components in frequency domain [1], first input and output parameters of the components should be selected in a wide range of frequencies. In most ANN models, it is desired to represent the parameters in terms of scattering parameters (
After training and verification of the EM-ANN model, based on the usage they can be used either in stand-alone mode or in integrated mode along with microwave circuit simulators. In integrated mode, there is a linear model subroutine that connects models to the simulator. This subroutine returns
In this example, we demonstrate the use of ANN techniques to develop a model for a family of coupled-line filters [35]. Here,
Model type | Neural network structure | Average training error (%) | Average testing error (%) |
---|---|---|---|
ANN model using 120 sets of training data | 6-40-4 | 0.897 | 0.989 |
ANN model using 40 sets of training data | 6-35-4 | 1.073 | 4.357 |
Final testing and training results of ANN model (from Ref. [35]).
The use of coplanar waveguides (CPWs) in RF and microwave-integrated circuits has brought many advantages. Accurate modeling of these components is necessary for accurate simulation of circuits. One of the fields that experts recently have been working on is toward the development of accurate and efficient methods for EM simulation of CPW discontinuities, but the challenge of using these tools for iterative CAD and circuit optimization [37] is the time-consuming nature of EM simulation. To overcome this problem, EM-ANN models have been suggested [38]. The models include CPW transmission line, short- and open-circuit stubs, step-in width discontinuities, and T-junctions. These EM-ANN models are linked to microwave circuit simulators and allow for the accurate and very fast EM circuit optimization in the framework of circuit simulator [1]. A general schematic of a coplanar waveguide is shown in Figure 14. In this figure,
Simple coplanar waveguide.
Progress in technology caused merging large number of microwave circuits and creating multilayer complexities that leads to investing much effort on optimizing and lowering the cost and weight of these circuits. Besides, accuracy and efficiency are important factors that should be satisfied in designs to have desirable simulation results. EM-ANN-based methodology despite other solutions that have been suggested was enormously successful in modeling Vias elements in microstrip circuits and multilayer Vias connectors [39]. Some other suggestions had limitations such as heavily computational expenses or limited range of frequency. As an example, microstrip transmission line model is one of the implemented ANN-based models in this case. In this model, input parameters are frequency which is in the range, log
Simple schematic of microstrip transmission line.
Output parameter is
Training data average error | 1.161 | 0.377 |
Training data SD | 1.157 | 0.376 |
Validation data average error | 0.774 | 0.293 |
Validation data SD | 0.875 | 0.223 |
Average and standard deviation (SD), for absolute error training (from Ref. [1]).
In this chapter, a review of some tools commonly used in RF/microwave simulation and modeling has been presented. In the last few decades, high-frequency effects have become an important factor in RF/microwave area. These effects can be found in all levels of design from tiny chips to packaging structures. In order to capture these effects, it is common to use physics-based models or electrical models which lead to large equations and large computational efforts for solving and simulating them, which is extremely time-consuming and expensive. Artificial neural networks recently have become popular among computer-aided design tools. The main topic in this chapter was a discussion on neural network which was mentioned as a powerful tool in modeling and simulation areas, also two main types of neural network structures including static and dynamic neural networks and their different types has been presented. In static neural network section, we talked about multilayer perceptron (MLP) and radial-basis function (RBF) structure, and in time-domain part we discussed recurrent neural network (RNN), dynamic neural network (DNN), state-space dynamic neural network (SSDNN), and adjoint state-space dynamic neural network (ASSDNN) methods. Other than neural network, as mentioned already, there are several numerical methods that are being used in the procedure of simulation and modeling microwave components such as Krylov method, finite-difference time-domain (FDTD), finite-element time-domain (FEDT), and vector fitting (VF). Here, we presented vector-fitting method that is widely used for modeling microwave and electromagnetic components with good performance. VF despite other system identification methods avoids ill-conditioning calculation, and because of this, it works more efficiently. Also, this method is very robust; it performs well even for high-order fitting and does not disturb by poorly selected starting poles. VF technique is very easy to implement in a computer program, since it is constructed upon matrices from simple fractions, and the problems in this case are easy to solve.
As a conclusion, the ANN-based methodologies and other mentioned methods are capable of applying to RF/microwave modeling and components simulation and are shown to have both speed and accuracy advantage for modeling nonlinear functions, despite many other conventional techniques.
Magnetic stimulation is the application of “electrode-free electrical stimulation” using induced current from a pulsed magnetic field. The magnetic field generated by the stimulation coil induces an electromagnetic-induced overcurrent in the body that resultantly depolarizes nerves and muscles’ cell membranes, thereby stimulating the brain, nerves, and muscles. Repetitive peripheral magnetic stimulation (rPMS) is applied as a treatment method that noninvasively delivers repetitive magnetic stimulation to peripheral nerves and muscles. It has attracted attention as a new means of rehabilitation, especially for sensory and motor disorders [1].
To date, neuromuscular electrical stimulation (NMES) has been widely used as a similar tool. NMES is an electrical stimulation therapy primarily performed to improve motor function, such as suppressing muscle hypertonia associated with upper-motor neuron damage, preventing and improving muscle atrophy associated with peripheral neuropathy, and increasing muscle strength. However, this therapy is associated with pain and discomfort caused by electrical stimulation. Compared with NMES, rPMS does not cause discomfort due to pain and can stimulate deep muscles [2]. NMES evokes cutaneous receptors and may generate noisy signals, whereas rPMS generates intrinsic receptive information during muscle contraction that affects brain reversibility [3].
Furthermore, rPMS does not require the application of electrodes and can be stimulated over clothing. Similar to NMES, however, rPMS is contraindicated for pacemakers and implantable medical devices. In addition, rPMS is larger in size and more expensive than NMES, making its widespread use a challenge. However, rPMS is expected to improve functional recovery in stroke patients because of its ability to safely stimulate deeper layers and improve muscle areas without pain [4].
rPMS can improve motor function in central nervous system (CNS) diseases. How, then, would the induction of CNS plasticity be altered by the parameters of rPMS? Nito et al. [5] studied the effects of rPMS on wrist extensor muscles in terms of neuroplasticity and motor performance in 26 healthy subjects (HS). Motor-evoked potential (MEP), intracortical inhibition (ICI), intracortical facilitation (ICF), M-wave, and Hoffman reflex were measured before and after the application of rPMS, and the effects of rPMS on wrist extensor movements were examined.
First, rPMS was applied to the wrist extensor muscles at different frequencies (50, 25, and 10 Hz), with the total number of stimuli set constant to examine the physical effects of stimulus frequency. MEPs of the wrist extensors increased significantly with rPMS at 50 and 25 Hz but remained unchanged at 10 Hz. In the next experiment, in which the number of stimuli was increased and the time required to induce plasticity was examined, at least 15 minutes of rPMS were required for 50- and 25-Hz rPMS. Based on these parameters, the sustained effect of 50- or 25-Hz rPMS was evaluated after 15 minutes of rPMS. Significant increases in MEP were observed up to 60 minutes after 50- and 25-Hz rPMS were administered. Similarly, attenuation of ICI and enhancement of ICF were also observed.
In addition, the maximal M-wave and Hoffman reflex were unchanged, suggesting that the imposition of rPMS does not directly stimulate the centrifugal nerves and excite the muscles but that the increase in MEP is caused by the plastic changes in the motor cortex. In addition, an increase in force and EMG during wrist extension movements was observed after the application of rPMS at 50 and 25 Hz. These results suggest that the application of rPMS at 25 Hz or higher for 15 minutes can increase cortical excitability at the irradiated site and improve motor output from the motor cortex, rather than changing the excitability of the spinal cord circuitry.
Recent studies have also reported the effects of rPMS in combination with noninvasive brain stimulation techniques and on regions other than the periphery. Kumru et al. [6] examined the effects of paired associative stimulation (PAS), in which paired stimuli of repetitive transcranial magnetic stimulation (rTMS) and rPMS are repeatedly applied. PAS is an effective method to induce plasticity in the human motor cortex. Three stimulus conditions were applied to 11 HS for 10 minutes each. In the rPMS alone condition, rPMS at 10 Hz was applied to the extensor carpi radialis (ECR) five times every 10 seconds for 60 trials. In the rTMS alone condition, rTMS was applied to the contralateral primary motor cortex region of the ECR at a frequency of 0.1 Hz (60 stimuli) and an intensity of 120% of the ECR threshold. In the PAS condition, rPMS and rTMS described above were performed with paired stimuli. The results showed that the PAS condition increased MEP amplitude and decreased ICI in the ECR. This suggests that PAS stimulation effectively increases corticospinal tract excitability and decreases ICI. Krause et al. [7] studied the effects of repetitive magnetic stimulation (rMS) to the right cervical nerve root (C7/C8) on corticospinal excitability in HS. The right cervical nerve root (C7/C8) innervating the test muscle, the right first dorsal interosseous muscle, was stimulated at a frequency of 20 Hz for 10 seconds with an intensity of 120% of resting motor threshold for a total of 10 trials. The results showed that rMS caused a significantly longer cortical silent period, increased ICI, and increased MEP amplitude. These changes were not confirmed contralaterally. This study confirmed that rMS increased MEP amplitude in the right first dorsal interosseous muscle without altering the left dorsal interosseous muscle. These results indicate that rMS affects motor cortex excitability similar to electrical stimulation; this suggests that rMS is applicable in spastic and central paraplegia rehabilitation.
As described above, physiological changes in rPMS have been reported in HS, and based on these studies, various clinical application studies have been conducted in the recent years.
Motor imagery (MI) is the simulation of movement in the brain without actual movement and is widely used in clinical practice as a tool for evaluation and treatment. Recently, the combined effects of MI and rPMS have been reported.
Asao et al. [8] examined the effects of rPMS combined with MI (rPMS+MI) on corticospinal excitability. The rPMS+MI condition and rPMS alone condition were performed on HS. In the rPMS+MI condition, rPMS was administered simultaneously with a cue for a MI task of dorsiflexion of the right wrist joint. The test muscle was the right ECR. The rPMS frequency was 25 Hz, stimulus duration was 2 s, and stimulus intensity was 1.5 times the motor threshold. In the rPMS alone condition, rPMS was administered under the same stimulation conditions as in the rPMS+MI condition. The results showed that the pre- and post-stimulus MEP ratios were more significant in the rPMS+MI condition than in the rPMS alone-intense condition, which was associated with Movement Imagery Questionnaire-Revised scores. This study suggests that an intervention combining rPMS and MI can induce more corticospinal excitation than rPMS alone.
The studies above did not clarify the effective length of intervention period for the combination of rPMS and MI to promote corticospinal excitability. Therefore, the time course changes in corticospinal excitability when rPMS and MI are used in combination have been examined [9]. rPMS alone, MI alone, and rPMS and MI combination conditions have been performed on HS. In the rPMS alone and rPMS+MI conditions, the ECR was stimulated with rPMS at 25 Hz for 2 seconds at a stimulus intensity of 1.5 times the motor threshold. In addition, the MI and rPMS+MI groups were asked to perform MI of wrist dorsiflexion for 2 seconds.
Consequently, the MEP amplitude increase of the ECR in the rPMS+MI group was observed after 10 minutes. In addition, the MEP amplitude after 20 minutes was more significant in the rPMS+MI group than in the rPMS alone group. This study suggests that the combination of rPMS and MI over 10 minutes increases corticospinal excitation and that the combined effect is more significant than rPMS alone. Overall, the combination of rPMS and MI may induce plasticity in the CNS and promote motor function recovery.
One of the clinical applications of rPMS is its muscle-strengthening effect. It has been reported that rPMS promotes muscle strengthening in animals and humans without causing pain.
Yang et al. [10] investigated the effects of neuromuscular magnetic stimulation (NMMS) on strength, cross-sectional area, and thickness of the quadriceps muscle in HS. NMMS was performed on the quadriceps femoris muscle at a frequency of 10 Hz and at the maximum tolerable intensity that could be tolerated for 15 minutes, thrice weekly for 5 weeks. The results showed that maximal isometric torque and mean peak torque increased significantly after intervention, but there was no change in cross-sectional area or thickness. This study suggests that NMMS effectively trains large or skeletal muscles such as the quadriceps.
Stolting et al. [11] showed that magnetic stimulation of a mouse muscle injury model caused post-traumatic muscle hypertrophy, but the effects of rPMS on human subjects remained unclear. Therefore, Hirono et al. [12] examined the acute changes in skeletal muscle thickness induced by rPMS after low-intensity exercise for clinical application of rPMS. rPMS was applied to the vastus lateralis muscle at the maximum intensity of the rPMS device after an HS performed three sets of 10 isometric knee extension exercises at 30% of maximum muscle strength. The results showed that the muscle thickness of the rectus femoris and vastus lateralis muscles after exercise increased over baseline values, with significant increases only in the vastus lateralis after rPMS. This study suggests that post-exercise rPMS induces muscle expansion
rPMS has the advantage of not causing pain and has been used in clinical practice with the expectation of functional recovery in some cases. Beck et al. [15] studied the effect of early intervention with rPMS on the vastus lateralis muscle after hip replacement surgery. The subjects were patients who underwent hip replacement after a proximal femur fracture. The experimental group received 10 Hz rPMS on the vastus lateralis muscle for 15 sessions daily, five times weekly for 3 weeks, whereas the control group received sham stimulation. The results showed that the root-mean-square value of the electromyogram during the maximum voluntary contraction of the vastus lateralis muscle after rPMS was significantly improved. Tandem rise time and normal walking speed in the rPMS group also improved. This study suggests that early intervention with rPMS on the lateral vastus muscle after hip arthroplasty improves muscle strength, standing balance, and gait function. This study also indicates that rPMS can be applied to patients with pain and wounds and is expected to be widely applied in clinical practice in the future.
As described above, rPMS, which promotes muscle strengthening without causing pain, has excellent potential for clinical applications.
Post-stroke hemiplegia occurs in more than 85% of individuals and 55–75% have residual upper limb dysfunction [16]. After stroke, the recovery rate to a practical level is approximately 60% for lower limb function and approximately 20% for upper limb function [17]. The effectiveness of rehabilitation and physical therapy for stroke has been reported in many cases. In this context, the effectiveness of rPMS for stroke has been reported in recent years.
rPMS is a noninvasive method of activating peripheral nerves at the stimulation site and improving muscle strength and has the advantage of being performed without causing pain. Jiang et al. [18] applied rPMS in the early subacute phase of stroke and studied its effect on severe upper limb disability. In the intervention group, rPMS of 20 Hz, totaling 2400 pulses, was applied daily for 2 weeks to the triceps brachii and extensor digitorum brevis muscles. The results showed that the rPMS group showed significant improvements in the upper limb, Barthel Index, upper limb muscle strength, and root mean square on the Fugl-Meyer Assessment compared with those in the control group. This study demonstrates that rPMS for the upper extremity after stroke improves upper extremity function and muscle strength.
Fernandez-Lobera et al. [19] studied the efficacy of rPMS as a tool to assess wrist spasticity in stroke patients. The subjects were HS, acute stroke patients without spasticity (AS), and chronic stroke patients with spasticity (CS). Spasticity was assessed by calculating the index of movement restriction (iMR) from the difference between the maximum passive movement range of the wrist joint and the evoked movement range by rPMS. The stimulation intensity of rPMS was set at 70% of the maximum output of the stimulator, frequency at 25 Hz, and stimulation duration at 2 seconds. The results showed that the amplitude, velocity, and acceleration of rPMS-induced movements were reduced in the CS compared with those in the HS and AS. The iMR values were 2.8 for HS, 13.0 for AS, and 59.2 for CS, with CS having the highest iMR value. Furthermore, the iMR value for CS decreased to 41.1 after treatment with botulinum neurotoxin.
Shoulder joint subluxation is one of the many complications following stroke and is an inhibitor of motor function recovery [20]. In particular, shoulder joint subluxation causes pain in the shoulder joint and has a significant impact on activities of daily living. Therefore, Fujimura et al. [21] investigated the effect of rPMS on shoulder joint dislocation caused by stroke. The subjects were patients who presented with shoulder joint subluxation after stroke. rPMS was performed repetitively on the supraspinatus, posterior deltoid, and infraspinatus muscles. Stimulation intensity was the maximum tolerable intensity and was performed at 30 Hz for 2 seconds for 100 sessions. Results showed that the acromion-humerus interval was significantly reduced after treatment. That shoulder joint pain, shoulder abduction range of motion, and upper extremity scores on the Fugl-Meyer Assessment also improved. This study demonstrates that rPMS for post-stroke shoulder dislocation decreases the degree of shoulder subluxation and pain and improves upper extremity motor function.
Krewer et al. [22] examined the short- and long-term effects of rPMS on spasticity and motor function in stroke patients. rPMS involved a total of 5000 stimuli at 25 Hz and a stimulus intensity of 110% of the resting motor threshold. Stimulation was applied to the extensor and flexor muscles of the upper arm and forearm twice daily for 2 weeks. Results showed short-term effects on wrist flexor spasticity (immediately after the intervention) and long-term effects on elbow extensor spasticity (2 weeks after the intervention) in the rPMS group. In addition, the rPMS group showed an improvement in sensory function. This study demonstrates that rPMS reduces spasticity and improves sensory function in stroke patients in both short and long terms.
Kinoshita et al. [23] investigated the effects of rPMS on the lower limb of chronic stroke patients on gait function. The subjects were stroke patients with lower limb hemiplegia and gait disturbance. The stimulation sites of rPMS were the gluteus maximus, vastus medialis, hamstrings, quadriceps, gastrocnemius, and soleus muscles of the paralyzed lower limb. rPMS was performed twice daily for 15 days at a frequency of 20 Hz for 3 s, 4800 pulses, and a stimulus intensity of 110% of the motor threshold. The results showed that walking speed, walking ability, and balance ability were significantly improved after the intervention. This study suggests that rPMS effectively restores gait function in stroke patients with gait disturbance.
Beaulieu et al. [24] studied the effect of rPMS on lower limb dysfunction in chronic stroke. The stimulation site of rPMS was the anterior tibialis muscle of the paralyzed lower extremity. rPMS was performed at a theta-burst frequency (three 50 Hz pulses each, delivered in 5-Hz bursts) for 190 s at 42% of maximum stimulation intensity. The results showed that the rPMS group increased ankle dorsiflexion range of motion and maximum isometric muscle strength after the intervention and decreased resistance to ankle flexor stretch. The results also suggested that these changes are related to residual corticospinal tracts. This study demonstrates that rPMS improves lower limb dysfunction in chronic stroke patients.
In conclusion, rPMS improves upper and lower limb dysfunction in stroke patients. Therefore, we believe that rPMS is a highly effective tool for evaluation and treatment in stroke rehabilitation.
This chapter outlines the physiological changes, combined effects of MI, muscle strengthening, and effects on stroke patients in rPMS. The rPMS parameters used in studies are listed in Tables 1 and 2. rPMS has attracted attention as a new neuromodulation technique that can noninvasively deliver repetitive magnetic stimulation to peripheral nerves and muscles using induced current from a pulsed magnetic field. Unlike NMES, rPMS is painless and has excellent potential for application in clinical settings. In particular, many clinical studies for stroke rehabilitation have been reported in recent years. Further development of rPMS research is expected, including its effectiveness when combined with other therapies and its integration with technology.
Stimulation site | Stimulation frequency | Stimulation intensity | |
---|---|---|---|
Nito et al. [5] | Wrist extensor muscles | 50 Hz, 25 Hz, 10 Hz | 120% of the motor threshold |
Kumru et al. [6] | Extensore carpi radialis muscle | rPMS: 10 Hz rTMS: 0.1 Hz | rPMS: 70% of motor threshold rTMS: 120% of motor threshold |
Krause et al. [7] | Cervical nerve roots (C7/C8) (First dorsal interosseous) | 20 Hz | 120% of motor threshold |
Asao et al. [8] | Extensore carpi radialis muscle | 25 Hz | 150% of motor threshold |
Asao et al. [9] | Extensore carpi radialis muscle | 25 Hz | 150% of motor threshold |
Yang et al. [10] | Quadriceps muscle | 10 Hz | Maximum tolerable intensity |
Hirono et al. [12] | Vastus lateralis muscle | 50 Hz | Maximum intensity of the device |
rPMS parameters in basic research on healthy subjects.
Patients | Stimulation site | Stimulation frequency | Stimulation intensity | |
---|---|---|---|---|
Beck et al. [15] | After hip replacement surgery | Vastus lateralis muscle | 10 Hz | Level where the visible movement of the knee is triggered |
Jiang et al. [18] | Early subacute stroke | Triceps brachii and extensor digitorum muscles | 20 Hz | Triceps brachii muscle: Intensity to induce 30° of elbow extension Extensor digitorum muscle: Intensity to induce 45° of wrist extension |
Fernandez-Lobera et al. [19] | Acute stroke without/with spasticity | Wrist extensor muscles | 25 Hz | 70% of the maximum stimulator power |
Fujimura et al. [21] | Shoulder subluxations caused by stroke | Supraspinatus and posterior deltoid/infraspinatus muscles | 30 Hz | Intensity until patients indicates that any further increase would become uncomfortable |
Krewer et al. [22] | Severe hemiparesis and mild to moderate spasticity resulting from a stroke or a traumatic brain injury | Extensors and flexors of the upper and lower arm. | 25 Hz | 110% of motor threshold |
Kinoshita et al. [23] | Hemorrhagic stroke with lower limb hemiparesis and gait disturbance | Gluteus maximus muscle, gluteus medius, hamstring muscle, musculus quadriceps femoris, gastrocnemius, soleus | 20 Hz | 110% of motor threshold |
Beaulieu et al. [24] | Chronic stroke | Tibialis anterior muscle | Theta-burst frequency (5-Hz bursts of three 50 Hz pulses each) | 42% of the maximal stimulator output |
rPMS parameters in clinical research.
This work was supported by the Yuumi Memorial Foundation for Home Health Care and JSPS KAKENHI Grant Number JP20K11173.
The authors declare no conflict of interest.
rPMS: | repetitive peripheral magnetic stimulation |
NMES: | neuromuscular electrical stimulation |
MEP: | motor-evoked potential |
rTMS: | repetitive transcranial magnetic stimulation |
PAS: | paired associative stimulation |
ECR: | extensor carpi radialis |
rMS: | repetitive magnetic stimulation |
ICI: | intracortical inhibition |
MI: | motor imagery |
NMMS: | neuromuscular magnetic stimulation |
HS: | healthy subjects |
AS: | acute stroke patients without spasticity |
CS: | chronic stroke patients with spasticity |
iMR: | index of movement restriction |
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He has written 96 scientific papers with more than 550 citations and editor/co-editor of 8 books and 37 book chapters an H index of 15.",institutionString:"Pantai Hospital Kuala Lumpur",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"4",institution:null},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7891",title:"Meniere's Disease",subtitle:null,isOpenForSubmission:!1,hash:"dda64b6335a1f85313b965777842c80a",slug:"meniere-s-disease",bookSignature:"Fayez Bahmad Jr.",coverURL:"https://cdn.intechopen.com/books/images_new/7891.jpg",editedByType:"Edited by",editors:[{id:"77351",title:"Prof.",name:"Fayez",middleName:null,surname:"Bahmad Jr",slug:"fayez-bahmad-jr",fullName:"Fayez Bahmad Jr"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8680",title:"Advances in Rehabilitation of Hearing Loss",subtitle:null,isOpenForSubmission:!1,hash:"068c9f4790e4843b9cbb8dcf86002bea",slug:"advances-in-rehabilitation-of-hearing-loss",bookSignature:"Diego Zanetti and Federica Di Berardino",coverURL:"https://cdn.intechopen.com/books/images_new/8680.jpg",editedByType:"Edited by",editors:[{id:"219687",title:"Dr.",name:"Diego",middleName:null,surname:"Zanetti",slug:"diego-zanetti",fullName:"Diego Zanetti"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7844",title:"Voice and Swallowing Disorders",subtitle:null,isOpenForSubmission:!1,hash:"9a81e27eb29c12553e9524f20a93b57d",slug:"voice-and-swallowing-disorders",bookSignature:"Monjur Ahmed",coverURL:"https://cdn.intechopen.com/books/images_new/7844.jpg",editedByType:"Edited by",editors:[{id:"206355",title:"Associate Prof.",name:"Monjur",middleName:null,surname:"Ahmed",slug:"monjur-ahmed",fullName:"Monjur Ahmed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7894",title:"The Human Auditory System",subtitle:"Basic Features and Updates on Audiological Diagnosis and Therapy",isOpenForSubmission:!1,hash:"a7c43fdbbbca096fea1c40c0a1928343",slug:"the-human-auditory-system-basic-features-and-updates-on-audiological-diagnosis-and-therapy",bookSignature:"Stavros Hatzopoulos, Andrea Ciorba and Piotr H. Skarzynski",coverURL:"https://cdn.intechopen.com/books/images_new/7894.jpg",editedByType:"Edited by",editors:[{id:"174266",title:"Prof.",name:"Stavros",middleName:null,surname:"Hatzopoulos",slug:"stavros-hatzopoulos",fullName:"Stavros Hatzopoulos"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7062",title:"Rhinosinusitis",subtitle:null,isOpenForSubmission:!1,hash:"14ed95e155b1e57a61827ca30b579d09",slug:"rhinosinusitis",bookSignature:"Balwant Singh Gendeh and Mirjana Turkalj",coverURL:"https://cdn.intechopen.com/books/images_new/7062.jpg",editedByType:"Edited by",editors:[{id:"67669",title:null,name:"Balwant Singh",middleName:null,surname:"Gendeh",slug:"balwant-singh-gendeh",fullName:"Balwant Singh Gendeh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7461",title:"Management of Tinnitus",subtitle:"The Enriching Views of Treatment Options",isOpenForSubmission:!1,hash:"9626e5a89247b934de503a3d08752e14",slug:"management-of-tinnitus-the-enriching-views-of-treatment-options",bookSignature:"Tang-Chuan Wang",coverURL:"https://cdn.intechopen.com/books/images_new/7461.jpg",editedByType:"Edited by",editors:[{id:"201262",title:"Dr.",name:"Tang-Chuan",middleName:null,surname:"Wang",slug:"tang-chuan-wang",fullName:"Tang-Chuan Wang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7131",title:"Selected Topics in Facial Nerve Disorders",subtitle:null,isOpenForSubmission:!1,hash:"0c16c1a947ded4fae51c047243593fbf",slug:"selected-topics-in-facial-nerve-disorders",bookSignature:"Isam Al-Zwaini and Mohammed Jalal Hussein",coverURL:"https://cdn.intechopen.com/books/images_new/7131.jpg",editedByType:"Edited by",editors:[{id:"30993",title:"Prof.",name:"Isam Jaber",middleName:null,surname:"Al-Zwaini",slug:"isam-jaber-al-zwaini",fullName:"Isam Jaber Al-Zwaini"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7245",title:"Challenging Issues on Paranasal Sinuses",subtitle:null,isOpenForSubmission:!1,hash:"67a331ebb2dd2b8f73228fa4daa7382f",slug:"challenging-issues-on-paranasal-sinuses",bookSignature:"Tang-Chuan Wang",coverURL:"https://cdn.intechopen.com/books/images_new/7245.jpg",editedByType:"Edited by",editors:[{id:"201262",title:"Dr.",name:"Tang-Chuan",middleName:null,surname:"Wang",slug:"tang-chuan-wang",fullName:"Tang-Chuan Wang"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:22,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"33875",doi:"10.5772/32762",title:"Cochlear Implants in Children: A Review",slug:"cochlear-implants-in-children-a-review",totalDownloads:4967,totalCrossrefCites:9,totalDimensionsCites:19,abstract:null,book:{id:"1393",slug:"hearing-loss",title:"Hearing Loss",fullTitle:"Hearing Loss"},signatures:"Julia Sarant",authors:[{id:"92583",title:"Dr.",name:"Julia",middleName:null,surname:"Sarant",slug:"julia-sarant",fullName:"Julia Sarant"}]},{id:"49574",doi:"10.5772/61835",title:"Classification of Hearing Loss",slug:"classification-of-hearing-loss",totalDownloads:5395,totalCrossrefCites:9,totalDimensionsCites:13,abstract:"Hearing loss is the partial or total inability to hear sound in one or both ears. People with hearing loss make up a significant 5.3% of the world’s population. The audiogram is an important tool used to determine the degree and type of hearing loss. This chapter presents hearing loss classification, which can aid in clinical diagnosis and help in finding appropriate therapeutic management. Hearing loss is classified based on ear anatomy, type of hearing loss, degree of the disease, and configuration of the audiogram. When the hearing loss is fully characterized, appropriate medical intervention can be assigned.",book:{id:"4654",slug:"update-on-hearing-loss",title:"Update On Hearing Loss",fullTitle:"Update On Hearing Loss"},signatures:"Waleed B. Alshuaib, Jasem M. Al-Kandari and Sonia M. Hasan",authors:[{id:"174550",title:"Prof.",name:"Waleed",middleName:null,surname:"Alshuaib",slug:"waleed-alshuaib",fullName:"Waleed Alshuaib"},{id:"174551",title:"MSc.",name:"Jasim",middleName:null,surname:"Al-Kandari",slug:"jasim-al-kandari",fullName:"Jasim Al-Kandari"},{id:"174552",title:"Dr.",name:"Sonia",middleName:null,surname:"Hasan",slug:"sonia-hasan",fullName:"Sonia Hasan"}]},{id:"49108",doi:"10.5772/61217",title:"Hearing Loss and the Voice",slug:"hearing-loss-and-the-voice",totalDownloads:3357,totalCrossrefCites:2,totalDimensionsCites:10,abstract:"The voice varies according to the context of speech and to the physical and psychological conditions of the human being, and there is always a normal standard for the vocal output. Hearing loss can impair voce production, causing social, educational, and speech limitations, with specific deviation of the communication related to speech and voice. Usually, the voice is not the main focus of the speech-language pathology therapy with individuals with hearing loss, but its deviations can represent such a negative impact on this population that it can interfere on speech intelligibility and crucially compromise the social integration of the individual. The literature vastly explores acoustic and perceptual characteristics of children and adults with hearing loss. Voice problems in individuals with this impairment are directly related to its type and severity, age, gender, and type of hearing device used. While individuals with mild and moderate hearing loss can only present problems with resonance, severely impaired individuals may lack intensity and frequency control, among other alterations. The commonly found vocal deviations include strain, breathiness, roughness, monotone, absence of rhythm, unpleasant quality, hoarseness, vocal fatigue, high pitch, reduced volume, loudness with excessive variation, unbalanced resonance, altered breathing pattern, brusque vocal attack, and imprecise articulation. These characteristics are justified by the incapability of the deaf to control their vocal performance due to the lack of auditory monitoring of their own voice, caused by the hearing loss. Hence, the development of an intelligible speech with a good quality of voice on the hearing impaired is a challenge, despite the sophisticated technological advances of hearing aids, cochlear implants and other implantable devices. The purpose of this chapter is therefore to present an extensive review of the literature and describe our experience regarding the evaluation, diagnosis, and treatment of voice disorders in individuals with hearing loss.",book:{id:"4654",slug:"update-on-hearing-loss",title:"Update On Hearing Loss",fullTitle:"Update On Hearing Loss"},signatures:"Ana Cristina Coelho, Daniela Malta Medved and Alcione Ghedini\nBrasolotto",authors:[{id:"174260",title:"M.Sc.",name:"Ana Cristina",middleName:null,surname:"Coelho",slug:"ana-cristina-coelho",fullName:"Ana Cristina Coelho"},{id:"174643",title:"Dr.",name:"Alcione",middleName:null,surname:"Brasolotto",slug:"alcione-brasolotto",fullName:"Alcione Brasolotto"},{id:"174644",title:"MSc.",name:"Daniela",middleName:null,surname:"Medved",slug:"daniela-medved",fullName:"Daniela Medved"}]},{id:"33864",doi:"10.5772/33569",title:"The Mongolian Gerbil as a Model for the Analysis of Peripheral and Central Age-Dependent Hearing Loss",slug:"the-mongolian-gerbil-as-a-model-for-the-analysis-of-peripheral-and-central-age-dependent-hearing-los",totalDownloads:2370,totalCrossrefCites:3,totalDimensionsCites:6,abstract:null,book:{id:"1393",slug:"hearing-loss",title:"Hearing Loss",fullTitle:"Hearing Loss"},signatures:"Gleich Otto and Strutz Jürgen",authors:[{id:"96191",title:"Dr.",name:"Otto",middleName:null,surname:"Gleich",slug:"otto-gleich",fullName:"Otto Gleich"},{id:"96195",title:"Prof.",name:"Jürgen",middleName:null,surname:"Strutz",slug:"jurgen-strutz",fullName:"Jürgen Strutz"}]},{id:"55472",doi:"10.5772/intechopen.69089",title:"Paranasal Sinus Anatomy: What the Surgeon Needs to Know",slug:"paranasal-sinus-anatomy-what-the-surgeon-needs-to-know",totalDownloads:5713,totalCrossrefCites:5,totalDimensionsCites:6,abstract:"Performing a smooth and clean sinus surgery goes hand in hand with a perfect understanding of the nasal and paranasal anatomy. Within this chapter, the paranasal and related structures surgical anatomy will be extensively reviewed, with emphasis on the anatomical landmarks and the normal anatomical variations, which have a significant impact on the function, pathology, and surgical procedures of the paranasal sinuses.",book:{id:"5911",slug:"paranasal-sinuses",title:"Paranasal Sinuses",fullTitle:"Paranasal Sinuses"},signatures:"Abdulmalik S. Alsaied",authors:[{id:"199716",title:"Dr.",name:"Abdulmalik",middleName:"Saad",surname:"Alsaied",slug:"abdulmalik-alsaied",fullName:"Abdulmalik Alsaied"}]}],mostDownloadedChaptersLast30Days:[{id:"63699",title:"Management of the Complications of Maxillary Sinus Augmentation",slug:"management-of-the-complications-of-maxillary-sinus-augmentation",totalDownloads:7815,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Dental implant rehabilitation of the posterior maxillary region has always been a challenging issue due to both alveolar ridge atrophy and sinus pneumatization. Maxillary sinus augmentation is a well-known and predictable procedure in vertical deficiencies of the posterior maxilla. To date, various techniques have been described based on the physiology of intrasinus bone repair to obtain better outcomes. Nevertheless, these procedures could also be associated with several intra- and postoperative complications such as perforation of the sinus membrane, hemorrhage, infection, graft resorption, and loss of the graft or implants. The aim of this chapter is to review the contemporary methods for maxillary sinus augmentation and to present both recommendations for prevention and management of the associated complications.",book:{id:"7245",slug:"challenging-issues-on-paranasal-sinuses",title:"Challenging Issues on Paranasal Sinuses",fullTitle:"Challenging Issues on Paranasal Sinuses"},signatures:"Alper Sindel, Mehmet Mustafa Özarslan and Öznur Özalp",authors:[{id:"244837",title:"Dr.",name:"Alper",middleName:null,surname:"Sindel",slug:"alper-sindel",fullName:"Alper Sindel"},{id:"244918",title:"Dr.",name:"Mehmet Mustafa",middleName:null,surname:"Özarslan",slug:"mehmet-mustafa-ozarslan",fullName:"Mehmet Mustafa Özarslan"},{id:"244919",title:"Ms.",name:"Öznur",middleName:null,surname:"Özalp",slug:"oznur-ozalp",fullName:"Öznur Özalp"}]},{id:"55472",title:"Paranasal Sinus Anatomy: What the Surgeon Needs to Know",slug:"paranasal-sinus-anatomy-what-the-surgeon-needs-to-know",totalDownloads:5708,totalCrossrefCites:5,totalDimensionsCites:6,abstract:"Performing a smooth and clean sinus surgery goes hand in hand with a perfect understanding of the nasal and paranasal anatomy. Within this chapter, the paranasal and related structures surgical anatomy will be extensively reviewed, with emphasis on the anatomical landmarks and the normal anatomical variations, which have a significant impact on the function, pathology, and surgical procedures of the paranasal sinuses.",book:{id:"5911",slug:"paranasal-sinuses",title:"Paranasal Sinuses",fullTitle:"Paranasal Sinuses"},signatures:"Abdulmalik S. Alsaied",authors:[{id:"199716",title:"Dr.",name:"Abdulmalik",middleName:"Saad",surname:"Alsaied",slug:"abdulmalik-alsaied",fullName:"Abdulmalik Alsaied"}]},{id:"69430",title:"Concurrent Rhinoplasty and Endoscopic Sinus Surgery",slug:"concurrent-rhinoplasty-and-endoscopic-sinus-surgery",totalDownloads:1201,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Combining rhinoplasty and endoscopic sinus surgery (ESS) was first reported in 1991 by Sheman and Matarasso. Since then, many authors have documented a large series showing the overall efficacy of combining the two procedures. The focus of this manuscript is to document the author’s recent experience with combining rhinoplasty and endoscopic sinus surgery and highlight the changes that have occurred during the author’s 2-years experience. A retrospective data review was performed on 53 (31 females and 22 men, age range 16–55 years) patients who underwent combined rhinoplasty and ESS between January 2016 and December 2018 at Pantai Hospital Kuala Lumpur by the same surgeon. The mean age was 31.8 years. All patients had severe nasal obstruction with chronic rhinosinusitis and were followed up for a minimum of 6 months post-surgery and underwent ENT workup, which included history, office rigid endoscopy, CT scans of paranasal sinuses and preoperative photography. Initially, the ESS was performed followed by the open rhinoplasty with or without osteotomy. The ESS consisted of middle turbinate reduction [15/53 (28.3%)], maxillary antrostomy [36/53 (67.9%)], ethmoidectomy [38/53 (71.6%)], frontal sinusotomy [7/53 (13.2%)], and sphenoidotomy [9/53 (16.9%)]. Most of the sinus symptoms resolved postoperatively with 47 (88.6%) of 53 patients describing their improvement as significant. Fifty (94.3%) of 53 patients stated that they would recommend the concurrent procedure. The benefits of these advances are illustrated by a review of the literature with good results (functional and cosmetic) and minimal complications.",book:{id:"7062",slug:"rhinosinusitis",title:"Rhinosinusitis",fullTitle:"Rhinosinusitis"},signatures:"Balwant Singh Gendeh",authors:[{id:"67669",title:null,name:"Balwant Singh",middleName:null,surname:"Gendeh",slug:"balwant-singh-gendeh",fullName:"Balwant Singh Gendeh"}]},{id:"49574",title:"Classification of Hearing Loss",slug:"classification-of-hearing-loss",totalDownloads:5388,totalCrossrefCites:9,totalDimensionsCites:13,abstract:"Hearing loss is the partial or total inability to hear sound in one or both ears. People with hearing loss make up a significant 5.3% of the world’s population. The audiogram is an important tool used to determine the degree and type of hearing loss. This chapter presents hearing loss classification, which can aid in clinical diagnosis and help in finding appropriate therapeutic management. Hearing loss is classified based on ear anatomy, type of hearing loss, degree of the disease, and configuration of the audiogram. When the hearing loss is fully characterized, appropriate medical intervention can be assigned.",book:{id:"4654",slug:"update-on-hearing-loss",title:"Update On Hearing Loss",fullTitle:"Update On Hearing Loss"},signatures:"Waleed B. Alshuaib, Jasem M. Al-Kandari and Sonia M. Hasan",authors:[{id:"174550",title:"Prof.",name:"Waleed",middleName:null,surname:"Alshuaib",slug:"waleed-alshuaib",fullName:"Waleed Alshuaib"},{id:"174551",title:"MSc.",name:"Jasim",middleName:null,surname:"Al-Kandari",slug:"jasim-al-kandari",fullName:"Jasim Al-Kandari"},{id:"174552",title:"Dr.",name:"Sonia",middleName:null,surname:"Hasan",slug:"sonia-hasan",fullName:"Sonia Hasan"}]},{id:"56237",title:"Caffeine and Meniere’s Disease",slug:"caffeine-and-meniere-s-disease",totalDownloads:1765,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Meniere’s disease is characterized by recurrent vertigo, fluctuating hearing loss, and persistent tinnitus. Caffeine consumption in modern society is a widespread and culturally accepted habit; however, there is no consensus about its mechanism of action in various organs and systems, including the auditory and vestibular. The few clinical studies have shown that abstention from caffeine has little effect in patients with Meniere’s disease, both in relation to vertigo, tinnitus and hearing loss.",book:{id:"5454",slug:"up-to-date-on-meniere-s-disease",title:"Up to Date on Meniere's Disease",fullTitle:"Up to Date on Meniere's Disease"},signatures:"Alleluia Lima Losno Ledesma, Monique Antunes de Souza\nChelminski Barreto and Carlos Augusto Costa Pires de Oliveira",authors:[{id:"68849",title:"Prof.",name:"Carlos Augusto C. P.",middleName:null,surname:"Oliveira",slug:"carlos-augusto-c.-p.-oliveira",fullName:"Carlos Augusto C. P. Oliveira"},{id:"175482",title:"Dr.",name:"Monique",middleName:null,surname:"Barreto",slug:"monique-barreto",fullName:"Monique Barreto"},{id:"194400",title:"Dr.",name:"Alleluia",middleName:"Lima",surname:"Losno Ledesma",slug:"alleluia-losno-ledesma",fullName:"Alleluia Losno Ledesma"}]}],onlineFirstChaptersFilter:{topicId:"192",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82305",title:"Nonreceptor Protein Kinases and Phosphatases Necessary for Auditory Function",slug:"nonreceptor-protein-kinases-and-phosphatases-necessary-for-auditory-function",totalDownloads:12,totalDimensionsCites:0,doi:"10.5772/intechopen.105425",abstract:"Phosphorylation is one of the most common posttranslational protein modifications. It has multiple roles in cell signaling during development as well as for maintenance of diverse functions of an organism. Protein kinases and phosphatases control phosphorylation and play critical roles in cellular processes from cell birth to cell death. Discovery of hearing-loss-associated gene variants in humans and the study of animal models have identified a crucial role of a plethora of protein phosphatases and kinases in the inner ear. In this review, those nonreceptor kinases or phosphatases are discussed, which are encoded by genes implicated in causing inherited hearing loss in humans or in mouse mutants. These studies have served to highlight the essential roles of protein kinases and phosphatases pathways to the function of the auditory system. However, the inner-ear-specific substrates for most of these enzymes remain to be discovered, as do the mechanisms of disease due to the variants in the genes that encode these proteins.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Sadaf Naz"},{id:"82266",title:"Structure and Physiology of Human Ear Involved in Hearing",slug:"structure-and-physiology-of-human-ear-involved-in-hearing",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.105466",abstract:"Hearing is the fundamental sense based on the normal functioning of the hearing organ “the ear,” which plays a vital role in social interaction and the ability of learning. The human ear is divided into three parts: the outer, middle, and inner ear. Defects in outer and middle ear can cause conductive hearing loss, while the defective inner ear may lead to sensorineural hearing loss. So, it is important to study the structure and physiology of the human ear. When a sound of particular frequency enters the outer ear, it passes through the auditory canal and strikes the tympanic membrane. It vibrates and passes these vibrations to three ossicles present in the middle ear. The ossicles amplify the vibrations of sound and send them to the cochlea in the inner ear. Cochlea contains organ of Corti, which converts these vibrations into electrical signals by its hair cells. The neural signals in turn are interpreted by the brain, which one can hear and understand. The aim of this chapter is to review the basic structure and physiology of different parts of the human ear that are involved in the hearing process.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Alishbah Sheikh, Bint-e-Zainab, Kanwal Shabbir and Ayesha Imtiaz"},{id:"82244",title:"A Short Overview on Hearing Loss and Related Auditory Defects",slug:"a-short-overview-on-hearing-loss-and-related-auditory-defects",totalDownloads:9,totalDimensionsCites:0,doi:"10.5772/intechopen.105222",abstract:"Hearing is the ability of a person to recognize sound in the surroundings and it makes communication possible. Ear is the human organ serving as a transducer that perceives signals from the environment and converts it into detectable forms for interpretation by the brain. The auditory system is among one of the most highly studied systems. Researchers have described the physiological function of the system in detail but due to its complexity, the genetic mechanisms and genes implicated in auditory function are still being revealed. Numerous studies on the genetics of hearing indicate hearing loss as one of the most common and prevalent disorders as it affects approximately five million people worldwide. Besides hearing loss, there are several other pathologies of auditory system which are common and have an established genetic basis. In this chapter, we will introduce the genetics of some common auditory pathologies including syndromic and non-syndromic hearing loss, auditory neuropathy, age-related hearing loss, and tinnitus. These understandings will 1 day lead to better diagnosis, management, and cures.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Hina Khan, Hafiza Idrees, Zunaira Munir and Memoona Ramzan"},{id:"81485",title:"Hearing Restoration through Optical Wireless Cochlear Implants",slug:"hearing-restoration-through-optical-wireless-cochlear-implants",totalDownloads:25,totalDimensionsCites:0,doi:"10.5772/intechopen.104622",abstract:"In this chapter, we present two novel optical wireless-based cochlear implant architectures: (i) optical wireless cochlear implant (OWCI) and (ii) all-optical cochlear implant (AOCI). Both the architectures aim to decisively improve the reliability and energy efficiency of hearing restoration devices. To provide design and development guidelines, we document their main components, discuss the particularities of the transdermal optical channel, and provide the analytical framework for their accurate modeling. Building upon this framework, we extract closed-form formulas that quantify the communication, the stimulation, and the overall performance. An overall comparison of OWCI and AOCI, as well as conventional cochlear implants, accompanied by future research directions summarizes this chapter. Our findings reveal that both the OWCI and the AOCI outperform conventional cochlear implant approaches; thus, they are identified as promising architectures for the next generation of cochlear implants.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos and George K. Karagiannidis"},{id:"80337",title:"Short-Latency Evoked Potentials of the Human Auditory System",slug:"short-latency-evoked-potentials-of-the-human-auditory-system",totalDownloads:55,totalDimensionsCites:0,doi:"10.5772/intechopen.102039",abstract:"Auditory Brainstem Responses (ABR) are short-latency electric potentials from the auditory nervous system that can be evoked by presenting transient acoustic stimuli to the ear. Sources of the ABR are the auditory nerve and brainstem auditory nuclei. Clinical application of ABRs includes identification of the site of lesion in retrocochlear hearing loss, establishing functional integrity of the auditory nerve, and objective audiometry. Recording of ABR requires a measurement setup with a high-quality amplifier with adequate filtering and low skin-electrode impedance to reduce non-physiological interference. Furthermore, signal averaging and artifact rejection are essential tools for obtaining a good signal-to-noise ratio. Comparing latencies for different peaks at different stimulus intensities allows the determination of hearing threshold, location of the site of lesion, and establishment of neural integrity. Audiological assessment of infants who are referred after failing hearing screening relies on accurate estimation of hearing thresholds. Frequency-specific ABR using tone-burst stimuli is a clinically feasible method for this. Appropriate correction factors should be applied to estimate the hearing threshold from the ABR threshold. Whenever possible, obtained thresholds should be confirmed with behavioral testing. The Binaural Interaction Component of the ABR provides important information regarding binaural processing in the brainstem.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Gijsbert van Zanten, Huib Versnel, Nathan van der Stoep, Wiepke Koopmans and Alex Hoetink"},{id:"80409",title:"Precocious Auditory Evoked Potential Recording with Free-Field Stimulus",slug:"precocious-auditory-evoked-potential-recording-with-free-field-stimulus",totalDownloads:65,totalDimensionsCites:0,doi:"10.5772/intechopen.102569",abstract:"The aim of this study is to determine the thresholds of normality in the recording of precocious auditory evoked potentials with free-field stimulation and to compare them with conventional stimulation with insertion headphones. For this purpose, we have carried out a case series study of children with normal hearing stimulated with insertion headphones, who underwent Auditory Brainstem Response (ABR) and Auditory Steady-State Response (ASSR) with free-field stimuli. Fifty-four ears with normal criteria of children between 6 months and 24 months of age were assessed. The latencies found with free-field stimulation in ABR were significantly longer than the latencies with insert earphone stimulation (p<0.05), and no differences were found in the inter-latencies. No significant differences were found in the thresholds of the ASSR response. We conclude that the ABR thresholds obtained in the free-field correspond to the delay due to the distance of the sound source to the eardrum and, therefore, are superimposable, being applicable to patients where it is not possible to stimulate with insert phones.",book:{id:"11232",title:"Auditory System - Function and Disorders",coverURL:"https://cdn.intechopen.com/books/images_new/11232.jpg"},signatures:"Juan Bautista Calero del Castillo, Alberto Guillén Martínez and Francisco García Purriños"}],onlineFirstChaptersTotal:9},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"