\r\n\tHistorically the word narcolepsy was first used by Ge´lineau in 1880 to describe irresistible episodes of sleep that were repetitive with short intervals. In 1950s Kleitman was the first individual to discover REM. Since then, laboratories that can record electrophysiological signals have been developed and possibilities for diagnosing, treating and monitoring sleep disorders have increased. However, narcolepsy can still be mixed with sleep disorders and neuropsychiatric disorders.
\r\n\tThis book aims at tackling narcolepsy from both basic science and clinical science perspectives. The reader will be able to grasp physiological mechanisms on one hand while associating narcolepsy with clinical diseases on the other. In narcolepsy there are disrupted night-day and sleep-wakefulness rhythms. Once this rhythm is hindered, the individual is confronted with biological, psychological and social problems. Narcoleptics are faced with the risk of collapsing and being knocked down to the floor while in kitchen or at the park, when driving in the traffic or walking down the stairs at any given moment.
\r\n\tThis book will not only provide a resource for physicians who will be helping this group of patients, but will at the same time contribute to the pathophysiology of the disease as it contains up to date information for researchers focusing on innovations in this field.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"5b19c98934802d13418f734de27786cd",bookSignature:"Associate Prof. Murat Kayabekir",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9780.jpg",keywords:"HLA types, Hypocretin System, Orexin Deficiency, REM Sleep, Familial Aspects, HLA-peptide, HLA DQB1*0602, Twin Studies, Sleepiness, Cataplexy, Sleep Paralysis, Hallucinations, Epworth Sleepiness Scale, SOREMPs, MSLT, CSF Hypocretin (Orexin), Behavioral Approaches, Pharmacologic, Children, Medication Side Effects",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 13th 2019",dateEndSecondStepPublish:"March 24th 2020",dateEndThirdStepPublish:"May 23rd 2020",dateEndFourthStepPublish:"August 11th 2020",dateEndFifthStepPublish:"October 10th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"265598",title:"Associate Prof.",name:"Murat",middleName:null,surname:"Kayabekir",slug:"murat-kayabekir",fullName:"Murat Kayabekir",profilePictureURL:"https://mts.intechopen.com/storage/users/265598/images/system/265598.jpg",biography:"Murat Kayabekir is an Associate Professor in the Department of Physiology at Atatürk University Medical School. He has completed Physiology training at Hacettepe University Medical School. He worked as a physiology specialist at the Sleep Disorders Centers and Electrophysiology Laboratory as a founder and director. His scientific fields of study are: neurophysiology, electrophysiology, sleep physiology and disorders, PSG and computer engineering, snoring sound analysis, sleep EEG and sleep spindles, innovative products, REM behavior disorders, narcolepsy, sleep apnea, bruxism, insomnia, and epilepsy during sleep.",institutionString:"Atatürk University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Atatürk University",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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A developing area is in epidural analgesia and anaesthesia, a technique employed for the relief of pain in both acute and chronic, and for anaesthesia to enable pain-free surgery. The aim of this chapter is to demonstrate several specific areas of research and how biomedical engineering techniques are used to improve and enhance the experience and training in the epidural procedure. The overall goal is to reduce the risks and subsequent morbidity in patients using advanced technologies to recreate the epidural procedure replicating as far as possible the in-vivo procedure. This would allow anaesthetists to practice the procedure in a safe and controlled environment without risk to patients. This could be achieved by recreating the sensation of the needle passing through the tissues and ligaments and by the generation of forces that match exactly those felt in-vivo. Epidural simulators are currently used as a training aid for anaesthetists, however existing simulators lack realism to various degrees and their operation is not based on measured in-vivo data that can accurately simulate the procedure. The techniques of advanced simulation and biomedical engineering detailed in this chapter can provide a solution.
Haptic devices have been used previously to reproduce needle forces but the forces are often not based on measured data. Needle insertion forces in-vivo are largely unknown as there are few studies in this specific area. Without accurate measurement of resultant pressure on the syringe plunger of the epidural needle, as the needle passes through the various ligaments and tissues of the spine, it is difficult to create accurate simulation of the epidural procedure. The ideal model would require other features such as a palpable spine, ability to accommodate for patient variation, 3D graphics visualisation and an adjustable needle insertion point. Techniques in biomedical engineering can provide solutions throughthe design of devices capable of making precise measurements and utilising them in a novel high fidelity epidural simulator. Adequate training on an advanced simulator will help alleviate the risks of epidural failures from inaccurate placement and also reduce potential morbidity to patients thereby improving the safety of the procedure.
This chapter is laid out in various sections to illustrate different aspects of current epidural anaesthesia research. Section 2describes the actual epidural procedure and its challenges. Section 3 discusses the needle insertion forces in epidurals. Section 4 describes an interspinous pressure measurement device for wireless data collection during needle insertion leading to a porcine trial discussed in Section 5. Section 6 describes an image processing technique for non-contact needle depth measurement that could be used in conjunction with pressure measurement for fully characterising the needle insertion. In Section 7, 3D-modelling of spine with bending and flexing is discussed for flexibility of patient’s positions together with heterogeneous volumetric modelling of spinal ligaments in Section 8. Stereo 3D visualisation for depth perception of epidural procedure has been discussed in Section 9. Section 10 applies a haptic force feedback device configured with the measured force data to create an electronic human–computer interface which is described in Section 11. Finally, section 12 brings all these technologies together and demonstrates the complete system that makes up our current epidural simulator prototype with conclusions provided in section 13.
Epidural analgesia and anaesthesia is commonly used as a form of pain relief during childbirth, for the treatment of chronic back pain or as a means to provide anaesthesia or analgesia during specific operations. Monitoring the depth of the needle during an epidural insertion is crucial because once the needle tip enters the epidural space, an epidural catheter is usually sited to a specific length. This enables the intermittent or continuous use of the epidural for anaesthesia or pain relief. If the needle is advanced too far it will puncture the dural sac and cause leakage of cerebrospinal fluid. Post dural puncture headaches may result, which can be extremely disabling for the patient. Other potential risks include nerve damage or bleeding which may very rarely lead to paralysis. If the needle is not within the epidural space, the analgesia or anaesthesia may be ineffective or absent due to incorrect placement of the catheter.
During an epidural insertion, the operator tries to perceive which tissue layer the needle tip is passing through by feeling the resistances on the needle. This is a process known as “haptic” feedback. A simulator can assist the development of this visuospatial awareness of spinal anatomy and ‘feel’ of the procedure to allow practice prior to attempts on patients. Not only will this serve to enhance patient safety but it also creates a safe and controlled environment in which to learn.
The procedure of inserting an epidural needle into the lumbar spine requires the operator to visualise in their mind a three-dimensional (3D) anatomical image of the bony alignments and the various tissue layers from skin, through to subcutaneous fat, supraspinous and interspinous ligaments, ligamentum flavum and then to the epidural space. Epidural needle insertion is essentially a blind procedure, but utilises a well-known technique referred to as “loss of resistance” (LOR). LOR essentially involves identification of the epidural space by compression of either fluid or air as the epidural needle encounters the various ligaments of the lumbar vertebral column [1]. Initially, the back of the patient is palpated, and using surface landmarks such as the iliac crests, an assessment is made of suitable intervertebral spaces and of midline. For lumbar epidurals, this may be between lumbar vertebra 3 (L3) and lumbar vertebra 4 (L4) for instance. The epidural or Tuohy needle, as it is commonly called, is inserted into the interspinous ligament and a syringe filled with saline is attached to the end of the needle. These LOR syringes are specially manufactured so that there is less friction between the plunger and the inner wall of the LOR syringe. A constant or intermittent force is then applied to the plunger by the operator’s thumb as the needle is slowly advanced forward. As the tougher and more fibrous ligamentum flavum is encountered, a higher resistive force to injection is encountered. Once the needle tip traverses the ligamentum flavum, the epidural space is then entered into and saline can be quite easily injected, hence the phenomenon of LOR. It is this haptic perception that informs the operator of needle location within the various tissue layers, obstruction from bone and loss of resistance from potential spaces such as those between the ligaments. Combining this with the creation in one’s mind of a three-dimensional image of lumbar spinal anatomy enables successful placement of an epidural catheter.
The ideal epidural simulator should be capable of replicating the above procedure and aim to recreate as far as possible the in-vivo procedure. A real Tuohy needle could be inserted at any intervertebral space in the lumbar or thoracic region using the midline or paramedian approach [2]. It would contain a force feedback haptic device, with force data originating from measured Tuohy needle insertions from patients. Using measured in-vivo data from patients and integrating this into the epidural simulator software, the resistance would automatically adjust to give patient variation on weight, height and body shape. This could simulate random patients or match measurements from a specific patient. The 3D virtual patient and virtual vertebrae can also be adjusted in size and shape to match measurements from actual patients. As the needle advances, the resultant force should represent each tissue layer and a LOR on reaching the epidural space. Once the epidural space is reached, saline would be released. During the entire insertion, a 3D virtual spine could be displayed on the monitor showing the trajectory of the needlein real time. The manikin could bend forwards to mimic spinal flexion to increase spacing between the vertebrae or alternatively bend backwards (extension) to simulate increased difficulty in locating the interspinous space for training purposes.
Variation in patient size, height, weightand other characteristics should be possible based upon actual patient measurements. Currently, most simulators have only two or three options such as obese, elderly and normal [3-6] which is perhaps not enough to encapsulate reality and could therefore be improved. Simulators could have unlimited patient variation by including parameters such as height, weight, body shape, age, obesity which could be adjustable. Ideally, the settings should match measurements from real patient data. The adjustments can be programmed to occur automatically based on basic patient data, so that the user does not have to manually configure all the settings. The simulator could then re-create a virtual model of a particular patient. Clinicians planning on performing the epidural can practice beforehand on a virtual model of the patient thereby reducing the learning curve during the procedure on the patient. The four common patient positions adopted during epidural insertion are sitting, sitting with lumbar flexion, lateral decubitus and lateral decubitus with lumbar flexion. These four common positions at least should be modelled in an epidural simulator to give a greater level of realism than static epidural simulators. Ideally, variable spine flexibility could be achieved by modelling 3D flexible spine vertebrae and extended to other positions to simulate difficult spinal anatomy. This may allow simulation of spinal conditions such as curvatures and rotations caused by kyphosis and scoliosis. These conditions cause difficulties in placing the needle due to unusually positioned landmarks. Also the accuracy of the forces in epidural simulators is a topic of recent discussion [7-9], so it is important that the forces required toinsert a needle during simulation match those achieved in reality. Skills learned during this simulation can then be transferred to the actual clinical environment.
Epidural insertion consists of a complicated interaction of many forces, needle position and intrinsic properties of the epidural equipment: a) Each tissue has various viscosity, elasticity, density and frictional properties. b) Bubbles of air in saline can compress. c) The method of insertion can vary depending upon needle inclination angle, paramedian angle, speed of insertion and twisting of the needle. d) Properties of the needle can vary, including the angle of the tip, tip type - side tipped or two-plane symmetric, needle gauge from 15-20 and width of the metallic walls in hollow needles vary. e) Plunger resistance is caused by friction on the inner syringe walls. f) The flow of saline is restricted by the funnel narrow opening of the syringe at LOR. g) The needle orifice can plug with tissue obstructing saline release.
Theoretically, a model can partition reaction force down into its individual constituents. The thumb applies force onto the plunger of the syringe and this force interacting with the frictional and resistive tissue forces contributes to the ‘resultant pressure’, see Figure 1. This pressure cannot escape so it causes the needle to push forwards. This causes the ‘reaction force’ which is equal and opposite to the applied force and comprised of several factors: a) The cutting force required for the needle tip to pierce the tissue. b) Friction caused by needle shaft resistance on the tissue. c) Static friction to get the stationary needle moving. d) Side compression force is caused by the surrounding tissues. e) Torque is caused by twisting of the needle. f) All of these forces, resistances and torque vary according to depth and tissue stiffness.
It is not feasible to measure all of these forces individually in-vivo and it would not make sense to measure the exact proportions of each force that make up the reaction force. In practice, it may be sufficient to measure the resultant pressure of the saline instead. Measuring resultant pressure provides a combination of all reaction forces, which is felt by the anaesthetist during insertion, and this combination of all forces is what simulators need to re-create to simulate the feeling on insertion.
Several forces involved with needle insertion
A sterile wireless measurement device was developed to record the resultant pressure of the saline inside the syringe during an epidural needle insertion. This measurement device is used to enable data collection to quantify the pressure during the epidural procedure. Quantifying the pressure will enable accurate configuration of an epidural simulator.
Our novel pressure measurement device has wireless functionality and by using entirely sterile components allows in-vivo trials to be conducted with patients. A wireless data transmitter is utilized to minimize the equipment and disruption in the hospital room (Figure 2).
Remotely monitored wireless epidural pressure measurement system.
The design aims to minimise changes to the standard epidural set up. A small sterile three-way tap (BD ConnectaTM) is connected between the Tuohy needle and syringe (Figure 3). The tap is connected to the pressure transducer via a one metre length of saline-filled sterile manometer tubing. The transducer’s electrical plug is connected by a short electrical cable to our wireless transmitter. At the remote site, a wireless receiver is connected via Universal Serial Bus (USB) to the computer.
The UTAH Medical Deltran disposable transducer is used for the pressure measurement sensor. These transducers are commonly used in hospitals to monitor systemic blood pressure and central venous pressure. Transducers produce a small electrical signal based on the pressure of the liquid inside the manometer tubing. Disposable transducers are designed to have accuracy of +/- 3% with the average output of 100.03 +/- 0.55 mm Hg and the worst cases being 98.53 and 101.36 when 100 mm Hg was applied [10].
Wireless Device for recording measured pressure of saline during insertion.
The computer can process pressure data, display a real-time graph on screen and simultaneously record the data to a file. When the anaesthetist presses on the syringe plunger, the saline inside the syringe is pressurised and the device quantifies this pressure. The computer runs our custom built software (Figure 4) which monitors pressure data as it arrives [11]. The software displays the live data on screen in the form of a real-time graph, can save graphs as images to file and writes data to a text file. The data files can be used for further analysis using statistical software. Before each insertion, the graph and start-time are reset and a new data file is created. Pressure can be converted into various units. In the current implementation the pressure is measured in mmHg or kPa and also a provision is given to determine force on the plunger in Newtons. This directly provides actual pressure measurement of saline inside the needle as applied to a continuum. To test this device a pilot trial was conducted on a porcine cadaver.
Screen print of the software to monitor and record pressure of saline during insertion
A trial using a section of a porcine cadaver was conducted to test the pressure measurement device during epidural insertions. The pig is claimed to be the closest animal model for human spinal research and can be a representative anatomical model for the human spineand tissues [12]. The porcine tissue specimen was a double loin saddle cut. The cadaver was obtained from a livestock farm within 24 hours of slaughter without being frozen or modified in any way to avoid desiccation and deterioration of the spinal tissues which would affect the pressure measurements. The pig was a standard hybrid Large White cross Saddleback. The specimen contained the entire back in one piece, with the whole spine, and all tissue layers from external skin, through to the thoracic cavity. The porcine tissue was mounted vertically against a wooden support to mimic sitting position, resting upon, but not attached onto, a platform beneath (Figure 5).
Epidural insertions were performed by two experienced anaesthetists. The epidural space was located using a Portex 16-gauge Tuohy needle (Smiths Medical International Ltd, Kent, UK) at L2/3 or L3/4 intervertebral levels using a midline approach. Subsequently a number of different vertebral levels ranging from T12-L5 were targeted. The porcine spine was palpated to locate anatomical landmarks prior to insertion. The Tuohy needle with its introducer stylet penetrates the skin as is standard procedure. The recordings of pressure were then started and continuously recorded throughout needle insertion until after the loss of resistance had been experienced.
Porcine cadaver set up for Tuohy needle insertions
The majority of insertions located the epidural space during the first attempt. Data from hitting bone was also recorded to analyse the effect on pressure. In some cases, the number of attempts to find the space was greater than three so those recordings were abandoned. The maximum pressure during ligamentum flavum was 500 mmHg. The highest pressures were obtained when the Tuohy needle hit bone.
The results demonstrated that during needle insertion the saline pressure started low and gradually built up, although the increase was not entirely steady due to the various tissues encountered. A similar pressure trend was found; a depression occurred on insertion 2 during 3-6 seconds and insertion 3 during 12-15 seconds(Figure 6, circular area). This may have been caused by the interspinous ligament and the pressure required to traverse this was 350 mmHg on insertion 2 and 470 mmHg on insertion 3. The final peak pressure was 500 mmHg which was caused by the ligamentum flavum (Figure 6, rectangular area). It wasalso noted that after the final drop of pressure there was often a ‘step’ before the bottom pressure was reached (square area). One explanation is that the initial pressure is the effect of opening up the epidural space which is a potential space and also saline pushing the dura away.
Pressure recordings durings two successful insertions to the epidural space.
The opinion of the trial anaesthetists was that porcine tissue did feel like a close approximation to human tissue and the shape of the graphs were similar to graphs previously reported from human insertions [8]. In most cases the resulting pressure-time graphs clearly show a drop when the loss of resistance occurred as the needle entered the epidural space (Figure 6). The maximum pressure peak during successful insertions ranged from 470 to 500 mmHg (62.7 - 66.7 kPa) caused by ligamentum flavum. After this the needle tip enters the epidural space causing a sudden loss of pressure back to the starting pressure. The shapes of each graph in successive trials were similar but also different to reflect individual variations.
The results of this pilot trial demonstrate that the wireless pressure measuring system is accurate and responsive in the porcine model. Such measurements from patients could be used to create realistic epidural simulators.
The reason why needle depth is important is that it relates the depths at which each resultant pressure occurred during the epidural procedure. This can also provide information about the depth of ligaments. We have developed image processing algorithms to measure the needle depth by a wireless camera during insertion [13].
During the epidural insertion procedure, the needle is slowly advanced through layers of tissue into the epidural space which is on average somewhere between 40-80mm deep. It is possible to record the depth of the needle tip by viewing the 10mm markings printed on the metallic needle; however, it is important to measure the needle depth precisely so that the needle travel can be guided with available measurements fromtechniques such as ultrasound scanning or magnetic resonance imaging for precise needle placement in the actual procedure. We have developed a novel image processing technique which aims to measure insertion depth of an epidural Tuohy needle in real-time. The implemented technique uses a single wireless camera to transmit depth data remotely to a host computer. Combining length and pressure data enables more accurate interpretation of the data in that the various changes in pressure can be linked to the actual depth at which the changes occurred.
The 16 gauge Portex Tuohy needle of 80mm length (Figure 7) is the most common epidural needle used in hospitals. The needle has grey and silver markings on the metallic shaft at 10mm intervals which are used by the software as a reference length. The blue handle is the plastic part at the base which is held by the operator and connected to a LOR syringe. This is used for colour detection.
Properties of the 80mm Tuohy needle used for image processing
The actual technique of length and size measurement by digital image processing is well established, however, in this specific circumstance, image processing is much more complex and challenging due to many reasons; (i) the needle is a thin, narrow object, (ii) the needle is composed of reflective stainless steel, (iii) the needle is circular in cross-section causing colour changes around the shaft of the needle, (iv) wireless camera introduces transmission noises, (v) as the needle is tilted it reflects in different directions, (vi) the needle will not be the only object in the foreground due to the operator’s hands and patient’s back, (vii) lighting conditions vary from room to room. We have overcome these problems by advanced analysing techniques focusing on a small area of the dynamic environment.
The actual technique involves placing a wireless camera in the procedure room, one metre away from the needle insertion, which will transmit data to a remotely located computer. The camera transmits a 640x480 pixel image in full colour over a 20MHz wireless link. The computer contains the image processing algorithm to detect the visible needle in the image and measure its length. The first step in the algorithm is to automatically calibrate the background model. For ten seconds, with no objects in the foreground, the colour values (HSV) for each pixel are analyzed. Maximum and minimum values are stored in an array and used later as a background model. HSV values from each frame are compared to the background model. Foreground objects are identified by HSV values outside of the expected range. The pixels from foreground objects are scanned for HSV values which match the blue handle. The centre point of the blue handle is found by taking an average and is stored for object tracking in subsequent frames, and is assumed to be approximately at the level of the needle shaft. The rightmost edge is stored and assumed to be the start point of the metal shaft. The blue handle is removed from further processing. The algorithm scans horizontally from the position of the blue handle to find the metal needle shaft by matching HSV values. The leftmost and rightmost pixels in the metal shaft are identified. These are stored for tracking in subsequent frames. At this point a strip of image remains over the needle. For each column, an average HSV value is taken. This average is used to create four separate histograms for H, S, V and the total along the length of the metallic shaft. The histograms identify sudden changes in colour, caused by the boundaries between 10mm markings (Figure 8). Histograms make the markings more detectable under reflective conditions. The number of visible 10mm markings is counted. The number of pixels in each division is counted to find how many pixels equate to 10mm. If the final marking is only partially visible the length is calculated by comparing it to a full division.
Output of the algorithm to measure needle length with histograms
The image processing algorithm was tested during insertions. The needle was successfully detected and measured accurately in most frames. The developed software was used to draw a graph of the length in real time and write the length data into a data file. Figure 9 shows the graph during an insertion in which the needle was slowly advanced and then rapidly withdrawn. We found that length measurement was accurate to within +/-3mm, when the needle was 500mm from the camera. The graph shows two erroneous readings at about 4 and 8 seconds, which was due to camera noise and in these frames the needle shaft was not detected properly, but all other frames were successfully measured and verified by the actual measurement. The total insertion took about fifteen seconds with 10 frames per second. The failure rate was 3 frames out of 150 which gave an overall 97.8% reliability during this insertion [13]. Errors like this could potentially be removed by ignoring sudden jumps in the data. The graph currently displays length but this can be converted from length to needle depth by simply subtracting the value from 80 mm, which is the total length of the needle.
Software showing plot of needle length during insertion
The distance between needle and camera can be varied because length is measured using the 10mm markings as a reference length. At distances over 150cm the reliability dropped but this could be improved with a higher resolution camera. The needle can be tilted up or down to +/-30 without any adverse effect to measurements. Tilting towards or away from the camera does not affect measurement as long as the divisions are clearly visible because division length differentiates between length reductions caused by tilt and caused by insertion. Failures occurred on some frames, due to blur in the image, or at certain angles where silver and grey areas became merged. The background model successfully removed the majority of background, even with cluttered multi-colour backgrounds.
In order to simulate the whole epidural procedurea realistic user interface must be provided together with the flexibility of 3D visualization and haptic interaction. The 3D models for the epidural simulator were generated with an object modelling software. Each vertebra is an individual wireframe model, constructed from 514 vertices. The vertices are positioned and then wrapped by a texture. Shadows and light sources are applied through OpenGL interfaces. The spine in the simulator contains 26 separate objects for the thoracic, cervical and lumbar spinal vertebrae, sacrum and coccyx. Layers of tissue, fat, muscle and skin were appended as layers above the bones. The different parts of the model were exported into separate format files. The format is text based with each vertex on a separate line. A custom C++ OpenGL graphics application then parses the text file to re-create each vertex. The epidural Tuohy needle was created as separate 3D models allowing it to be moved around independently. This is important to allow the operator to place the needle anywhere along the spine for training purposes.
The 3D objects can be viewed as stereograms (Figure 10) by displaying two images of the same object side by side with slight rotation around the Y axis [14]. The epidural simulator also supports this method of stereo in addition to page-flip stereo.
Stereogram view of the spine model with two perspectives and binocular parallax
Transparency is applied to skin, subcutaneous fat, supraspinous ligament, interspinous ligament and ligamentum flavum. This allows the user to see the position of the needle tip in the tissue layers. Transparency can be adjusted during the simulation by a control on the keyboard. Rotation is enabled allowing the camera angle to rotate around the scene. This is applied by OpenGL translation and rotation which gives an effect of camera movement whilst the other objects all remain stationary. During rotation, the tip of the needle remains at the central focus point of the screen. Zoom can be applied to move closer or further away from the site of insertion in the working epidural simulator. Pan can also be applied which is a translation of the camera which allows the user to view other areas or to move up and down the spine when selecting the insertion site.
Another issue equally important is the flexibility built into the spine model. There are four common patient positions adopted during the administration of spinal or epidural anaesthesia [15]. Lateral decubitus (Figure 11) involves lying down sideways on the patients left or right, usually the right side is used for caesarean patients, because it is the opposite side from which the patient will lie on during surgery in the left lateral tilt position, which helps to increase the spread of anaesthetic. When the patient lies in lateral positions their back should be close and parallel to the edge of the bed, with their spine in a straight line. However, a variation to this position, maximal lumbar flexion in the lateral decubitus position can be used. The sitting position is preferred and often required in obese patients to enable the palpation of spinal processes and identification of the midline. Finally, the sitting position combined with maximal lumbar flexion is also used, and having the patient bend forward is advantageous to the anaesthetists because it increases the space between the vertebrae, which increases the target space for the needle to pass through.
Four common patient positions used for epidural insertion
Based on this, the patient could bend the spine to various positions, so the epidural simulator is required to use computer graphics models of the human spine which can bend, flex and twist. The model can realistically duplicate the shape of the spine during various sitting positions adopted by patients during surgery and epidural anaesthesia. The extent of bending and flexing is kept within the limits of human spine flexibility. Also the model vertebrate adapt in size to match weight and height of specific patient bodies based on parametric modelling [16]. Our spine model is flexible for epidural simulation which offers accurate models of spinal vertebrae.
The human spine consists of twenty six vertebrae. Each of the vertebrae connects with numerous ligaments. Internally, there is a protective space running through the centre of the spine, housing the spinal cord. The column of vertebrae also provides connection points with the ribs and back muscles. The twenty six vertebrae are segmented into five regions, each with varying characteristics. From cranial to caudal there arecervical vertebrae (C1-C7), thoracic vertebrae (T1 – T12), lumbar vertebrae (L1 – L5), sacrum and coccyx. The human spine is able to bend, flex and rotate in various directions. Lumbar flexion occurs when the patient bends forwards and lumbar extension occurs when bending backwards. The spine was modelled using 3D design software, formed from 26 individual vertebrae, shown in Figure 12. The 26 vertebrae were each loaded as 3D models into a custom made software graphics application. The software renders 3D objects using vertices with the OpenGL graphics library and its utility toolkit (GLUT). The colours of each region of vertebrae bone, flesh and the spinal discs were set using materials.
The model spine consisting of 26 individually rendered 3D vertebrae
Initially the vertebrae are positioned in the standing position and are then adjusted by mathematical equations to match the current patient position. The curvature of the spine for four common patient positions was calculated using the equations. The shape of the spine was based on the four common patient positions used for epidural insertion. Our model’s prediction for the spine shape for each of the positions is shown in Figure 13 [14].
The spine model with flexion for four common patient positions
The ability to flex and rotate the spine has provided the opportunity to simulate epidural insertions on patients in various positions. This is important because the feeling of insertion is different for each patient position. This novel aspect has not been attempted in epidural simulation before and will increase versatility of the simulation.
Since the introduction of traditional computer graphics and modelling techniques, the primary focus has been to display and modelling of homogenous objects which have uniform interior and consist of one material throughout. This was acceptable for many situations, however, such surface-based approaches were aimed to represent the visual appearance of the external layer of objects, leaving the interior untouched. Recently, with the availability of increased computing power, the focus has shifted from surface-based to volume-based graphics, whereby volume-based architecture attempt to describe the material structure of internal regions by the use of voxels [17]. This can allow manipulation and experimentation on the physical properties of the materials, such as density, friction, elasticity, tensile strength and in so doing opens up new possibilities for experimentation. Heterogeneous objects are a step further, being solid physical objects, which consist of two or more material primitives but offering the advantage of materials that may be distributed continuously blending with each other.
For epidural needle insertion, the needle passes through several ligaments along its path to the epidural space, with each of the ligaments having different properties such as density, resistance to insertion and friction (see Section 3). A model is required to encompass these aspects of each ligament if the graphics are to be capable of displaying a true likeness of the materials in-vivo. Ligamentum flavum (LF) is heterogeneous in nature, containing both elastic tissue and fibrous tissue. Certain data describing the ligamentum flavum has been recorded in the literature and can be used to set up a heterogeneous model of the ligament. As LF thickness increases, fibrosis increases and elastic tissue decreases. The dorsal side of LF contains more fibrous tissue and less elastic tissue than the dural and middle sides, as indicated by a fibrosis Score of 1.58, 1.63, and 2.63 for dural, middle, and dorsal sides respectively [18]. The loss of elastic fibres caused by increased thickness is more pronounced along the dorsal side. A single patient has several ligamentum flava, one at each spinal level between the lamina and their thicknesses vary according to the spinal level. A study of 77 patients measured LF at spinal level L2/3, L3/4, L4/5, and L5/S1, the mean LF thickness is 2.41, 3.25, 4.08, and 2.68 mm [18]. It was shown that the thickest part of ligamentum flavum is consistently at L4/5, which is the level that endures the greatest mechanical stress. LF is crescent shaped in cross section on the horizontal plane with the thickest part in the middle. It wraps around the circular epidural space and dura. It connects to lamina above and below. The elastic fibres are yellow in colour, hence ‘flava’ being Latin for yellow. Each flava is a separate ligament which is clearly seen from the side of the lamina.
Object modelling software was used to create a model of the vertebrae. At the location of L2/L3 a ligamentum flavum was modelled with the thickness 2.41mm which was internally comprised of bundles of fibres (Figure 14).
The modelled ligamentum flavum between L2/L3 vertebrae.
The interior structure of the ligamentum flavum has been modelled by numerous bundles of fibres extending vertically and parallel to one another, as do the elastic and fibrous tissues in-vivo. By creating this heterogeneous model of the internal structure of ligamentum flavum, the model will describe more accurately how the material responds to a needle being inserted through it. Similar models may be created for interspinous ligament and supraspinous ligament which are also both heterogeneous in nature, consisting of over three types of elastic fibres that can used to provide realistic haptic feedback.
We have applied stereoscopic 3D computer graphics for visualization of epidural insertions. The stereoscopic images are viewed through a head mounted visor containing two OLED micro-displays in stereo using the page-flipped method. The 3D graphics are built from several vertex models of the anatomical structures as described in section 7. The stereo simulation allows depth to be perceived so that the operator can judge depth of the needle tip in relation to tissue layers and bones, which aids to the location of the epidural space. Applying stereoscopic vision to epidural simulators helps the operator to visualize the depths required for correct needle placement in the epidural space [14].
Depth judgement is crucial to the technique and since stereographics allows the perception of depth in 3D graphics, epidural simulators can benefit greatly from stereo-technology. Here the aim is to apply stereo vision technology to simulate epidural needle insertion. Without stereo graphics the depths of objects in simulations are not perceived accurately. By viewing 3D graphics on a flat computer screen there is no way of knowing the actual distance between objects other than by estimating their size. Estimation is not always accurate and some medical applications may require far more precision in depth perception. Epidural simulators require the needle tip to penetrate several layers of tissue between 42-47mm thick and must stop within the 6mm epidural space [19], which is difficult to achieve without depth perception. With stereo vision, distance can be perceived natively allowing the user to intuitively view the depth and distance between objects by perceiving differences between the two images, if images are appropriately scaled.
Stereo glasses contain two small OLED screens, one for each eye. Alternatively, glasses can be polarized, which allows viewing of a polarized screen, which has both images superimposed, one of which arrives at each eye. Shutter glasses can be used which contain moving mechanisms to consecutively close each eye similar to a camera shutter. The screen then displays images for left and right eye consecutively at the same shutter speed. Alternatively, a glasses free approach, vertically dispersive holographic screen (VDHS) can be used by directing two beams of light containing the images into each eye separately [20]. Mirror screens contain two monitors mounted at 110 degrees with a plane of silver-coated glass combining the two images and cross-polarized glasses are worn to separate the images. For all stereo systems, once the two images arrive separately at each eye, the brain combines them to generate 3D with depth perception based on some calibrated data.
For this epidural simulator, we have used stereo glasses containing two OLED micro-displays, one for each eye, with magnifying lenses. Figure 15 shows how the epidural simulator is being used with the stereo glasses displaying the 3D spine model. The glasses have advantages that the user can see the image whichever direction they look in and as they turn their head motion detectors can rotate the image to follow. The glasses produce a 40-degree diagonal field of view for each eye. The image appears the same size as a 105 inch projection screen viewed from 12 feet. Magnifying lenses allow the eye to focus further away avoiding eye strain. The graphic resolution must be fixed at 800x600 pixels which display sufficient details. Two separate images are displayed on each eye display. Stereo is achieved by using the page-flip method. A signal is generated by the graphics card at 60Hz, with the images consecutively swapped between left eye and right eye. The swapping is done by the graphics card drivers. The hardware inside the 3D glasses splits this into two separate 30Hz signals and delivers one to each eye, this results in stereoscopic images.
Stereo glasses used for epidural insertion visualization
The epidural simulator software interfaces with head motion detectors. When the user turns their head, the 3D objects rotate by the same degree in the opposite direction to create an illusion of camera rotation. This interface allows the user to change the view point to different directions by turning their head, so that the mouse and keyboard are no longer required. The feedback from experienced anaesthetists suggested that the flexible spine model will be useful for modelling patient position. The options for adjustable body shape and size was seen as a positive step to encapsulate the variety of patients which has not previously been accomplished.
Haptic devices have become a more popular and accepted tool for medical simulation and provide an accurate way of re-creating the feel of surgery [21, 22]. The insertion of an epidural is a procedure which relies almost entirely upon feeling the forces on the needle. Epidural simulators are therefore ideallysuited to haptic technology. This section describes methods for configuration of a haptic device to interact with 3D computer graphics as part of a high fidelity epidural simulator development program.
Haptic devices have been used in epidural simulators previously, although they are not based on measured patient data from needle insertions. Instead, they are configured by ‘experts’ trialling and adjusting the system. It is therefore hard to assess the accuracy of the forces generated and so creates a real potential for improvement. The haptic device has currently been set up to reconstruct the force data found during the porcine trial. The force data from the graphs were divided into sections to represent each of the tissue layers separately [23].
A haptic device has been connected and used as an input to move the needle in 3D, and also to generate force feedback to the user during insertion(Figure 16). A needle insertion trial was conducted on a porcine cadaver to obtain resultant pressure data (Section 5). The data generated from this trial was used to recreate the feeling of epidural insertion in the simulator. The interaction forces have been approximated to the resultant force obtained during the trial representing the force generated by the haptic device. The haptic device is interfaced with the 3D graphics (see Sections 7-9) for visualization. As the haptic stylus is moved, the needle moves on the screen and the depth of the needle tip indicates which tissue layer is being penetrated. Different forces are generated by the haptic device for each tissue layer as the epidural needle is inserted. As the needle enters the epidural space, the force drops to indicate loss of resistance. An advantage to the use of haptic devices for epidural simulators is that they can accept various adjustable settings, so that patient variation including weight, height, age and sex can be accounted for, which helps to train for a range of patients. Patient variety is becoming an even more important aspect than ever since the current obesity epidemic poses great challenges for the anaesthetist. In obese patients, the depth to the epidural space is increased, anatomical landmarks are harder to feel and the midline is more difficult to locate. The resultant effect is that the risk of injury is increased.
The haptic device interfaced with the graphics
To apply different forces to each layer, 3D vector regions were defined within the graphics model. As the needle tip enters these regions, the software identifies which tissue layer the needle is in, based on the depth data from the trial (Table 1). The software then uses a lookup table to find the appropriate force for each layer, and instructs the haptic device to generate that force. The forces generated represent the resultant pressure on the syringe which is a sum of all resistances to insertion, which are the equal and opposite to the force applied by the user. For example, if a particular layer has insertion force of 4.3N, and the user is pressing with only 3.2N, then the haptic device exerts 3.2N, so the stylus remains stationary. Only if the user increases the force to over 4.3N the stylus will move forward. Table 1 is based on measurements taken from our porcine trial in line with [24].
The haptic device is also able to simulate palpation of the lumbar region. Palpation is the process for choosing which location to insert the needle. The haptic device was configured for palpation by creating a surface hardness profile of the lumbar region, with a hardness value for each point in the region (see Section 8). The haptic device can be used to press at any point and the user can feel the hardness at that point. This allows the user to locate landmarks and choose a point to commence needle insertion. Our advanced haptic interface is based on the measured data and the aim is to develop a generic simulator based on measured data to offer a realistic in-vitro experience before attempting the procedure on actual patients.
With the above developed components, a hardware device has been created consisting of a regular Portex LOR syringe connected to the computer via a serial data transfer device. This allows a regular clinical syringe to be used as part of an interactive system for the epidural simulator development. The syringe was also combined with the haptic device to create a comprehensive human-computer interface. The simulator can measure force applied to the plunger and the resultant pressure of the saline inside the syringe barrel. This interface enables a real clinical syringe to interface with a 3D graphical visualization showing the simulated insertion of the Tuohy epidural needle through the spinal ligaments.
The developed hardware interface makes use of the equipmentas developed in Sections 4&6 by incorporating custom made hardware with the developed software and the graphical visualization of the needle insertion procedure. The hardware device takes measurements of the forces applied onto the needle and the resultant pressure of the saline inside the barrel of the syringe caused by the pressure from the operators thumb on the plunger. The measurements are sent to the computer by a custom-made hardware interface device (see Sections 4&6). The graphical simulation uses these measurements to update the needle in the simulation and calculates the needle position. The graphical software calculates if any collisions have occurred between the needle and any bone structures, plus the resistance of insertion to saline, and the force required for the needle to move forwards through the current ligament.
The developed human-computer interface uses an actual syringe and an epidural Tuohy needleas shown in Figure 17. During insertions, the LOR syringe is normally connected directly onto the Tuohy needle. We have introduced a three-way tap between the needle and syringe. This connects onto a one metre length of saline manometer tubing which runs to a disposable pressure transducer. The transducer converts the pressure of the saline into an electrical signal. The electrical signal is connected into a hardware device which amplifies and sends the pressure reading to the computer. This allows the graphics visualization to update according the pressure applied by the operator’s thumb on the plunger of the syringe. This has the advantage that the user can control the visualization with the same equipment that would be used in-vivo, which is a more natural interface than simply using keyboard or mouse. Additionally, since the saline line separates the hardware device from the needle, the user can move the needle around since it is attached only by the saline line.
The syringe connected to the computer as an input device.
The hardware device runs at 8MHz. Data is transmitted from the hardware device to the computer using the serial RS232 port. The serial bit rate is running at 22000 bits per second. The serial data transfer protocol uses -12V DC as a positive bit and +12V DC as a negative bit. The serial transfer cycle starts with a negative start bit, followed by 8 data bits sent consecutively and finished with a positive stop bit. As shown in Figure 18, the following start bit can then occur either immediately or after a pause of arbitrary length.
Binary serial data transfer protocol.
The 8 data bits are received and interpreted as binary and converted into a decimal number from 0 to 255 for use in the software. The decimal value represents the pressure of the saline between 0 to 70 kPa, which is 0 to 550 mmHg. The 256 possible values give an accuracy resolution to within +/- 0.14 kPa. This can be easily increased to 1024 with 10 bits data transfer which will then provide accuracy of within +/- 0.03kPa. The speed could also increase beyond the current 22000 bits per second but it does not seem necessary since no delay is noticed between pressing the plunger and seeing the results on screen. Currently at 22000 bits per second the time delay between bits is 45μS so the start bit is identified by testing the pin for +12V, and then checking again after 22μS for the same high value. The computer runs the custom designed software which monitors the data as it arrives. Also the values are received by the graphics application which updates the visualisation to match the pressure applied on the physical syringe.
This study has demonstrated the development of a human-computer interface based around a clinical Portex LOR syringe connected via a custom made hardware interface device to a computer for use in an epidural simulator. The results show that the device is both fast and accurate enough to be used seamlessly in the simulation. The addition of the Portex LOR syringe with a pressure monitoring device has undoubtedly improved the human-computer interaction. Using the actual medical components in the implementation is beneficial because epiduralists will be familiar with the syringe and use it to interact with the 3D graphics visualization intuitively. The interface could be modified to be bi-directional i.e. the graphics software could send back data to the device which could control a motor to cause forces whichaffect the physical needle so that the user can feel the forces through the needle as in-vivo.
The presented biomedical engineering ideas have enabled us to develop a simulator with a combination of engineering, computing and clinical technologies as discussed in previous sections above. Data from the developed measurement devices have been used to configure a realistic force feedback epidural simulator [25]. Numerous improvements have been identified that could enhance existing epidural simulators. Manikin models are generally static and only able to represent one or two patient variations, such as normal and obese. An advanced simulator would be able to simulate insertions on a variety of body mass indices because excess fat deposition has the potential to generate very different changes in patient characteristics.
The developed system offers a virtual reality based epidural simulator (Figure 19) incorporating a 3D graphically modelled spine complete with skin, fat and tissue layers, supraspinous, interspinous ligaments and ligamentum flavum. In the current prototype, a Novint Falcon haptic device is used in combination with a Portex LOR syringe connected as a human-computer interface via a custom made electronic serial interface. As the haptic stylus is moved, the needle follows on the screen in 3D in real time. When pressure is applied to the plunger by the operator’s thumb, this is displayed in the graphic model. As the needle is advanced through the tissues, the forces are generated by the haptic device to reconstruct the feelings of needle insertion through each tissue layer. The forces of the needle insertion are based on the recorded forces measured during the clinical trial, and this data based approach is more accurate than previous simulators which have used a user evaluation approach to configure the forces.
Prototype 3D graphics epidural simulator with haptic device interface
Novel aspects of our epidural simulator include stereo graphics, modelled vertebrae, spine flexibility, patient variation, haptic force feedback based on measured needle insertion data, custom made syringe interface. The simulated needle can be inserted at any spinal position from T2 – L5 and needle direction from midline to paramedian.The 3D graphics allow a close-up real time view of the needle internally during insertion. The virtual patient can adjust to various body shapes, weights and heights since body size considerably affects insertion force. These all have roots in biomedical engineering that can potentially enhance many clinical procedures.
The application of biomedical engineering approaches can help simplify many clinical problems as demonstrated for the epidural procedure.
We havedescribed in this chapter, the developed measuring devices which have successfully recorded the data on resultant pressure and depth of epidural Tuohy needles during insertions in a porcine model. These data are very useful in developing a realistic high fidelity epidural simulator. We aim to measure pressures in-vivo with obstetric patients in labour of differing body mass indices and integrating this data with ultrasound and MRI scan imaging data. It is our belief that the resulting epidural simulator based on such data will replicate the in-vivo procedure more accurately since it is going to be based on patient specific information. No such simulator exists at the present time.
The overall benefits of applying biomedical engineering techniques to this research are that we are able to achieve a high degree of accuracy and improved technology for replicating the epidural procedure. By achieving higher realism and accuracy of simulation, epiduralists will be better trained with the procedure and this in turn will improve patient safety by minimizing the risk of failure and harm to patients.
The globe needs urgently to resort another option of sources of energy as a result of the rapid world energy supply exhaustion [1]. As a result of the depletion in oil, the world global warming and the effects of greenhouse making the earth on the condition of alarming [2]. Despite seeing the world are completely dependent on the limited sources of fossil-based petroleum that can later not withstand to meet future demands.
The world depletion fossil fuel happened, resulting in the continual price rising and the pressure for independence of oil and environments concerns lead to strong markets for biofuel [3]. The utilization of natural resources fuel leads to the vast side problem. The rapid increased of CO2 level in the environment resulted in the global warming resulting to the negative results of the burning of fuel from petroleum-based [4]. The worlds are concern about the climatic change and the consequent need to decreasing of greenhouse emissions gasses leading to the encouragement of the usage of bioethanol as an alternative or replacement [5]. Another challenge is as a result of the arise waste dumping in an open place resulting in malignant to the natural habitat at surrounding environments of the dumpsite. The concept of producing energy in the form of a solution by utilization of the waste is affordable, cheap and efficient. Recently, an enormous number of renewable sources of energy is rapidly growing technologies of renewable energy including solid biomass, liquid fuels and biogases [6]. A biofuel is a generated fuel through biomass rather than the one produced from the formation of the geological process of oil and fossils fuel. As a result of biomass can be technically utilized directly as fuel. The term biofuel and biomass are interchangeably used. Biomass with complex or free sugar that can later form soluble sugar is used for the production of bioethanol. The feedstock is divided mostly into three major groups; starchy crops, (sugar crops and by-products of sugar refineries) and lignocellulosic biomass (LCB), they differ respectively from the sugar solutions in them [7]. Production of bioethanol from the conventional feedstock like starch-rich feedstocks (corn, potato) and sugarcane has been previously reported as the first-generation process. Nevertheless, they have economic and social barriers [8]. Bioethanol second-generation process is gaining momentum. Lignocellulosic biomass (corn stover, sugarcane bagasse, straws, stalks and switchgrass) are used for the second-generation process. One of the significant alternative processes of bioethanol production with easy adaptability of this biofuel to prevailing engines with better octane rating [9, 10]. Any plant material with significant amounts of sugar is utilized as a source of raw materials in bioethanol production. Sugarcane, pineapple and potato are one of the major plants that resulted in a high yield of bioethanol as byproducts due to the presence of a high amount of sugarcane in it [11] (Figure 1).
The amount of bioethanol production depends on the substrate used as shown in the figure above. Adapted from Khandaker et al. [
Yeast is described as basidiomycetous or ascomycetous fungi responsible for reproducing through fission or budding and formed spores which are not enclosed in the fruiting body [13].
Fresh citrus fruits are consumed or the citrus juice is mostly preserved which it’s in ready form of consumption or concentrated form. After the extraction of citrus fruit juice, the remaining parts of the fruits serve as a rich source of lignocellulosic material and also utilized as a raw material for the fermentation of bioethanol. Simultaneous saccharification and fermentation from plantain, banana and pineapple peel through the cultured of
The present study observed that the maximum temperature and pH for the banana peels fermentation was 30°C and 6. With these maximum conditions of temperature and pH, different concentrations 3 and 12% of yeast were utilized for performing fermentation. The study found the period for the whole fermentation to complete reduced drastically [16]. The high glucose content in pineapple and orange resulted in the excellent yield of bioethanol [11] (Figure 2).
Percentage of sugar composition in various fruits and vegetables [
Rotten, peels, shells and a scraped portion of vegetables is one kind of biodegradable vegetable waste that generated in large amounts, usually dumped on ground for rotten near the household area. This act not emits an obscene odor but also creates a big irritation by attracting pigs, rats and bird as well as vectors of various human diseases. Vegetable waste mainly generates during the processing and packaging of vegetables, after preparation of cooking and post-harvest losses due to lack of storage facilities. Bioethanol can be produced through fermentation under controlled conditions. Microbial decomposition of vegetable waste generates bioethanol with high humus content. Many researchers have stated that vegetable waste is carbohydrate-rich biomass one of the potent substrates of renewable energy generations.
Research on the usage of fruit and vegetable wastes for the manufacture of biofuel is fetching attractive in different countries. Sulaiman et al. [17] abstracted a halal biorefinery for the production of bioethanol and biodiesel and value-added products in Malaysia. Vegetable wastes arise throughout the supply chain from the producer to consumer and vary widely depending on its harvesting, processing and marketing [18]. Vegetable waste can be raw, cooked, inedible and edible; parts are generated during production, harvesting, precooling, grading, storage, marketing and consumption at the consumer place. All the cut-down vegetable waste goes to landfill. Landfills spread offensive smells, produce methane which is a common greenhouse gas, and also produced a large amount of harmful leachate that can contaminate water and soil. Nevertheless, microbial digestion of vegetable waste can be used to produce bioethanol, renewable bioenergy. Vegetable waste has chemical potentials due to the high amount of saccharide in the form of lignocellulose. Promon [19] reported that vegetable waste as a high source of lignocellulose could be hydrolyzed into D-xylose and glucose.
Vegetable waste is a renowned nonedible source of lipids, amino acids, carbohydrates, and phosphates [20, 21]. All of these nonedible lignocellulose biomasses can also use for the production of bioethanol. Lignocellulose contains of 30–50% of cellulose, 20–40% of hemicellulose and lignin around 10–15% [22]. Cellulose is the main assembly of lignocellulosic built biomass which is a glucose homologous polymer associated by b-1,4 glycosidic bond [23]. After, glucose and other simple sugars production from all the sugar sources, the bioconversion endures till bioethanol is produced. Vegetable waste is widely used raw material for the production of bioethanol because it contains hemicellulose and cellulose, which can be changed into sugar by the hydrolysis method in presence of microorganisms [24]. The sugar content in vegetable waste extracts around 5% [25]. Yeast, fungi and bacteria can be used for the fermentation process [26].
Pretreatment: The pretreatment is the most costly and complicated step in the conversion of LCB into ethanol. The LCB in cellulose is usually sheathed or coated by hemicelluloses resulting in hemicellulose complex cellulose that works as a chemical barrier and attacked and prevent the chances of complex enzymes under its natural condition [27]. The complexes cellulose-hemicellulose are further subjected encapsulated with signs leading to the production of physical, physical barrier to the biomass of hydrolysis to produce fermentable sugars [28].
Chemical pretreatment: Primarily acids and alkali working on the biomass of the delignification, the degree of decreasing of crystallinity of cellulose and polymerization. HNO3, H3PO4, HCl and H2SO4 are utilized during acid pretreatment of biomass in the process the major alkali used is NaOH. Pretreatment of acid is applied in the stabilization of the fraction of hemicellulosic in the biomass, thereby making cellulose enzymes more accessible [29]. Physical pretreatments: This process convert the biomass through the increased surface accessibility area and pore volume, decreased in the degree of the polymerization of cellulose, hydrolysis of hemicellulose, partial depolymerization of lignin and its crystallinity. Physicochemical pretreatment: The exploitation of the usage of conditions and chemical compounds that affect the chemical and physical properties of the biostimulants including a large number of technologies example fiber explosion ammonia, steam exploitation, CO2 explosion, ammonia recycling percolation wet oxidation, soaking aqueous ammonia etc. Similarly, other pretreatments methods like technologies from physicochemical also increased the accessibility area surface of the enzyme biomass, cellulose crystallinity decreased and removal of lignin and hemicellulose during pretreatment.
Biological pretreatment: Microorganisms are used are utilized particularly fungi as brown rot, white rot and soft fungi rot, the most efficient among them are white fungi rot. The above treatment became effective through the alteration of the cellulose and lignin structure and separates them from the lignocellulosic matrix. While white, soft rot and brown rot fungi attack cellulose and lignin [30].
Detoxification: Pretreatment is an important aspect of converting LCB into ethanol.
It has a significant effect on the complete process leading to the generation of lignocellulose-derived by-products under the conditions of pretreatment such as acetic acid, sugar acids, levulinic acid, formic acid, furfural and hydroxymethyl furfural acts as enzymes inhibitors for the microorganisms fermentation for the subsequent stage if the accumulation is sufficiently high [31].
Inhibitors can be checked out by:
Chemical approach: by addition of alkali such as NaOH, reducing agents such as (sulfite, dithionite and dithiothreitol) Ca(OH)2, NH4OH, Reducing
Treatment using enzyme: peroxidase, laccase
Vaporization and heating: heat treatment, evaporation
Extraction using liquid–liquid: Supercritical fluid extraction such as (Trialkylamine, supercritical CO2), Ethyl acetate,
Extraction using liquid–solid: Lignin, Ion exchange and Activated carbon,
Treatments using microbes: thermospheric,
Hydrolysis: Hydrolysis is described as an industrial process where hemicellulose and cellulose present in the feedstock are converted to fermentable sugars. The fermentable sugars are maltotriose, maltose, sucrose, glucose, fructose they are generally accounting to 60–70% of the total solid dissolved. Enzymatic hydrolysis, alkaline or either acid is utilized in the conversion of cellulose and hemicellulose into their monomers sugar.
Acid hydrolysis is the oldest technology for cellulose biomass conversion to ethanol [32]. The acid hydrolysis is basically classified into two: concentrated acid hydrolysis and dilute acid. The diluted acid procedure is conducted through high pressure and temperature with a reaction time scale of one minute, reactivating continues process. The procedure of the concentrated acid utilized relatively low pressure and temperature with a much longer reaction time [33] (Figure 3).
Dilute acid hydrolysis flow chart of recovery bioethanol [
Dilute acid hydrolysis the following method it is used for hydrolysis of hemicellulose and as a cellulose pretreatment to make it most accessible for the enzymes. However, both the polymers of carbohydrate are hydrolysed using acid dilution under two stages, hydrolysis process: the following stage is carrying out at a minimum temperature to utilized the hemicellulose conversion as the fraction of hemicellulose biomass for the depolymerization at a low temperature than the portion of cellulose due to the difference in the structure between these two polymers of carbohydrate [34]. The dilution of acid involved a process of a solution of sulfuric acid 1% concentration in a reactor with continues flow at a temperature of 215°C [35]. Most of the process of the acid dilution to a sugar recovery is limited to efficiency of about 50%. The most paramount challenge in the hydrolysis of acid dilution is the raising of glucose yields greater than 70% in a viable economical industrial process with a maintaining high rate of cellulose hydrolysis with minimization of decomposition of glucose. Shrinking bed reactor countercurrent technologies have been 100% success in the yielding of glucose from cellulose [36].
Concentrated Acid Hydrolysis the method provide rapid and complete cellulose of hydrolysis to glucose and sugars of hemicelluloses to 5-carbon with a little bit of degradation. The concentration of the acid process utilized mild temperature relatively, the pressure created from the pumping pressure from vessel to vessel is utilized. Dilution acid process is shorter than the reaction time [35]. Depolymerization of the cellulosic fraction is the next step. Soaking and dewatered of solid residue from the first stage was carried out in 30–40% sulfuric acid for 50 minutes. For furthering of cellulose hydrolysis is carried out at 373 k [37]. Recovery of higher sugar efficiency was the primary advantage of the concentrated acid process [38]. The process of concentrated acid offers significant cost reduction than the process of dilute sulfuric acid [39].
Alkaline hydrolysis the major significant from pretreatment of alkali is the removal of lignin, which greatly improved the reactivity of the remaining aspects of polysaccharides [40]. In the biomass, the aligning structure is altered by glycosidic and ester degrading side chains of the biomass through the alkaline solvents, resulting in swelling as well as cellulose decrystallization [41]. Hydrolysis of alkaline is a very slow process that requires neutralization and the recovery of the added alkali is needed. Hydrolysis of alkaline is very suitable for agricultural residue and herbaceous and woody biomass is not suitable due to its high contents of lignin [42]. Previous experiments results confirmed that hydrolysis of alkaline has the highest reaction rate, followed by hydrolysis of acid and finally degradation of hydrothermal from the glycosidic bond cleavage insoluble water carbohydrate concerned. In other to the obtained significant yield of sugar by hydrolysis of alkaline, it is very challenging as a result of dimeric and mono carbohydrates such as fructose, maltose, cellobiose or glucose are attacked severely by the temperature of alkali at 100°C [42].
Enzymatic hydrolysis for enzymatic hydrolysis to take place it required the feeds to be hydrolysed by the enzyme to become fermentable sugars. Breaking down of cellulose take place using three types of enzymes β-glucosidases, cellobiohydrolases and endo-β-1,4-glucanases. The most effective and promising among them is the enzymatic process due to the specificity of the enzyme on the substrate relatively working on the minimum temperature and generating lower inhibitors. LCB enzymatic done usually by using either microorganisms producing an enzyme that secrets directly on the enzymes during their developments in the media or enzymes system that are commercially available where the latter is widely utilized and more feasible. The commercial-scale of cost-effective ethanol its major challenge is the enzymes costs [43]. The type of biomass and the conditions of hydrolysis is the major factors dependable for the conversion of lignocellulosic biomass to fermentable sugars. Many factors are solely responsible for the yield of sugar during hydrolysis of the enzyme. The factors are generally divided into two groups. (1) factors related substrate, and interlinked with one other (2) enzymatic and factors related process. Enzymes hydrolysis is the saccharification preferred method as a result of its; high yield, high selectivity, minimum energy cost and operating milder condition than other processes [14].
Fermentation process: Bioethanol production largely depends on three processes which are simultaneous saccharification and fermentation, (SSF) and simultaneous saccharification and co-fermentation (SSCF) and separate hydrolysis and fermentation (SHF). Ethanol fermentation is completely separated lignolistic hydrolysis in SHF fermentation. Hydrolysis enzymatic separation and fermentations enabled the operation of the enzymes at a higher temperature and excellent performance. The organisms in the fermentation process operate at a lower temperature for sugar utilization optimization. SSCF and SSF fermentation and hydrolysis process occur concurrently to keep the glucose concentration low, the whole process occurs in a short process. While the SSF fermentation pentose is separated from glucose while SSCF pentose and glucose are in the same reactor [44]. Both SSCF and SSF are more efficient and preferred over the SHF as a result the operation of the later cannot be performed on the same reactor [37].
Batch, fed-batch, repeated batch or continuous mode are important technology of bioethanol fermentation. Hadiyanto et al. [45] stated that the substrate is provided at the early stages of the process without removal or addition of the medium in a batch process. The process is known as the simple system of a bioreactor with a flexible, multi-vessel and Cassy control system. In a closed-loop system with high inhibitors and sugar concentration at the beginning and ends of the fermentation is maintained and the process carried out with high product concentration [46]. Complete sterilization, require fewer labour skills, can control easily, very easy to manage feedstocks, and flexible to various product specifications are benefits of the batch system [47]. However, the productivity of the system is very low and need intensive and high labour costs. Both inhibitions of growth of the cells and production of ethanol may come from the presence of significant amount/ high concentration of sugar in the fermentation chamber [48]. However, Fed-batch fermentation overcomes the inhibition and enhanced production of ethanol. In Fed-batch fermentation, combine a form of batch and continuous modes are operated which involves increasing substrate to the fermenter devoided removing it from the medium. The size of culture in fed-batch varies significantly, but the substrate must be fed with the right component properly at a certain rate. When the low substrate concentration is maintained, higher ethanol yield in feb-batch is observed. This is because low substrate concentration permits the smooth conversion of a reasonable amount of fermentable to ethanol [47]. The benefits of this feb-batch include; higher ethanol yield, greater dissolved oxygen in the fermentation chamber, Low fermentation time and medium component exhibit a low toxic effect [48]. Fed-batch is successfully operated in non-uniform SSF system by repeatedly adding pretreated feedstock to achieve comparatively high sugar and ethanol yield [14].
Continuous operation is achieved by unceasing addition of culture medium, substrate and nutrients to bioreactor embodied active microorganisms. In continuous operation mode, the culture size is kept constant and the end products of fermentation are siphoned from the media continuously. Discrete product types such as ethanol, cells and residual sugar could be accessed from the top of bioreactor [14]. The advantages of continuous system over batch and fed-batch; small size bioreactor, higher ethanol yield and cost-effective. However, shortcomings of this technique are; the greater tendency of contamination than other types [37]. The capability of
Bioethanol fuel has the following intrinsic quality: high-octane number; this measure the engine performance (Table 1). The more the octane number the higher compression that the fuel can endure before ignition. Higher octane number qualifies fuel to be used in high-performance gasoline engines that need compression ratios to be high. Hence, the use of gasoline with a low octane number causes the engine knocking [49]. It drastically decreases the emission of substances that are a threat to human health eg. CO (Table 2). The utilization of ethanol does not employ engine modification, it does not emit CO2, the cost of production is low, and it is eco-friendly, hence flipside of the solution to global environmental contamination [50, 51].
Bioethanol fuel property | Advantages | References |
---|---|---|
High oxygen content (35% w/w) | i. Increased combustion efficiency ii. Reduced hydrocarbon and carbon monoxide emissions | [52, 53] |
High octane number (107) and high latent heat of vaporization (0.91 MJ/kg) | i. Prevents premature ignition and cylinder knocking ii. Spontaneous ignition in internal combustion engines when bioethanol petrol blends are used | [54, 55] |
Low energy content (21.2 MJ/dm3) | i. Increased compression ratio ii. Decreased burn time iii. Increased power | [56, 57] |
Advantage of bioethanol.
Bioethanol | Fossil ethanol |
---|---|
Renewable | Non-renewable |
Waste plant material used as feedstock | Fossils source |
Cost-effective | expensive |
Least pollutants are released | Many pollutants are released |
Difference between bioethanol and fossil ethanol.
Temperature: the roles of temperature for
Effect of temperature on bioethanol yield [
Whereas low temperature slows the growth rate of cells which may be due to their low tolerance to ethanol at lower temperatures [62, 63].
Effect of Feedstock Concentration: feedstock encloses nutrients for microorganism’s growth during the fermentation process. At high feedstock concentration, the rate hydrolysis is speed up because more compound is bound to enzymes’ active site. With fixed number of enzymes and low amount of substrate cause decrease in production of ethanol because bound to enzymes’ active site. A small amount of ethanol will be obtained because of low substrates bound to the enzyme’s active site. Hence, the increase in feedstock concentration favors the production of ethanol [64] (Figure 5). However, according to Lin et al. [58] prolong exposure to a higher concentration of feedstock lead to diminishing the production of bioethanol.
Effect of feedstock on bioethanol production [
Effect of pH: Fermentation process is pH sensitive. In an acidic medium with moderate pH, high ethanol production was observed (Figure 6). Moderately acidic pH, cell permeability to some essential nutrients is influence by the concentration of H+ in the fermentation broth [28]. It has been experimentally observed that both growth and survival rate of
Effect of pH on bioethanol production [
Time of Fermentation: the rate at which growth of microorganisms occurs is affected by fermentation time (Figure 7). The shorter the fermentation times the more inefficient fermentation due to inadequate microorganisms growth. Equally, longer fermentation time cause affects
The production of ethanol by
Agitation rate this controls to regulate the entry of nutrients from the fermentation broth to inside cells and eviction of ethanol from the cells to the fermentation broth. Higher rate of agitation leads to higher production of ethanol. It plays a role in triggering sugar takes up and the inhibition of ethanol to the cell is reduced. The frequently used agitation rate for fermentation by yeast cells is 150–200 rpm. It is inadvisable to use excess agitation rate as it reduces metabolic activities of the cell and hence, unsuitable for smooth production of ethanol [28].
Inoculum concentration does not have any significant effect on the production of ethanol but the ethanol consumption rate and sugar yield [69]. When the is an increase in the number of cells from 1 × 104 to 1 × 107 cells per ml, increased ethanol production is also observed. It has been reported that when Inoculum concentration exceeds 107 and 108 cells per ml, no significant effect on the ethanol production observed [28]. At the elevated concentration of inoculum, reduction of fermentation time is observed as there is rapid cell growth.
The total results revealed the vegetables and fruits waste could be utilized for the production of bioethanol from recycled agricultural waste and management process. The discussions showed that bioethanol optimum yield is produced at pH 4, the temperature at 32°C and using 3 g/L yeast. The engine cars utilized efficiently bioethanol produced from waste rotten pineapple because it does not have high content and any dangerous elements. The principle or idea of using vegetables and fruits waste to produce bioethanol will aid in keeping the environment clean from the waste of agriculture. The process helped in overcoming to the challenges of depletion of fossil fuel with the creation of bioresearch energy. Bioethanol produced from the agricultural waste of vegetables and fruits is of good qualities with making the engine to produce less emission. Vegetables and fruits waste are good economical choice for the production of bioethanol because of its low cost and availability.
The authors wish to acknowledge the support of Center for Research Excellence and Incubation Management (CREIM), Universiti Sultan Zainal Abidin (UniSZA), Malaysia, Gong Badak, 21300 Kuala Nerus, Terengganu Darul Iman, Malaysia and KPT Grant (Project code: FRGS/1/2019/WAB01/UNISZA/02/2).
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