I Recent Advances in Biomedical Engineering Recent Advances in Biomedical Engineering Edited by Dr Ganesh R Naik In-Tech intechweb.org Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-prot use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2009 In-teh www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published October 2009 Printed in India Technical Editor: Zeljko Debeljuh Recent Advances in Biomedical Engineering, Edited by Dr Ganesh R Naik p. cm. ISBN 978-953-307-004-9 V Preface Background and Motivation The eld of biomedical engineering has expanded markedly in the past ten years. This growth is supported by advances in biological science, which have created new opportunities for development of tools for diagnosis and therapy for human disease. The discipline focuses both on development of new biomaterials, analytical methodologies and on the application of concepts drawn from engineering, computing, mathematics, chemical and physical sciences to advance biomedical knowledge while improving the effectiveness and delivery of clinical medicine. Biomedical engineering now encompasses a range of elds of specialization including bioinstrumentation, bioimaging, biomechanics, biomaterials, and biomolecular engineering. Biomedical engineering covers recent advances in the growing eld of biomedical technology, instrumentation, and administration. Contributions focus on theoretical and practical problems associated with the development of medical technology; the introduction of new engineering methods into public health; hospitals and patient care; the improvement of diagnosis and therapy; and biomedical information storage and retrieval. Much of the work in biomedical engineering consists of research and development, spanning a broad array of subelds. Prominent biomedical engineering applications include the development of biocompatible prostheses, various diagnostic and therapeutic medical devices ranging from clinical equipment to micro-implants, common imaging equipment such as MRIs and EEGs, biotechnologies such as regenerative tissue growth, and pharmaceutical drugs and biopharmaceuticals. Processing of biomedical signals, until a few years ago, was mainly directed toward ltering for removal of noise and power line interference; spectral analysis to understand the frequency characteristics of signals; and modeling for feature representation and parameterization. Recent trends have been towards quantitative or objective analysis of physiological systems and phenomena via signal analysis. The eld of biomedical signal analysis has advanced to the stage of practical application of signal processing and pattern analysis techniques for efcient and improved noninvasive diagnosis, online monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Techniques developed by engineers are gaining wider acceptance by practicing clinicians, and the role of engineering in diagnosis and treatment is gaining much deserved respect. The major strength in the application of computers in biomedical signal analysis lies in the potential use of signal processing and modeling techniques for quantitative or objective VI analysis. Analysis of signals by human observers is almost always accompanied by perceptual limitations, interpersonal variations, errors caused by fatigue, errors caused by the very low rate of incidence of a certain sign of abnormality, environmental distractions, and so on. The interpretation of a signal by an expert bears the weight of the experience and expertise of the analyst; however, such analysis is almost always subjective. Computer analysis of biomedical signals, if performed with the appropriate logic, has the potential to add objective strength to the interpretation of the expert. It thus becomes possible to improve the diagnostic condence or accuracy of even an expert with many years of experience. Developing an algorithm for biomedical signal analysis, however, is not an easy task; quite often, it might not even be a straightforward process. The engineer or computer analyst is often bewildered by the variability of features in biomedical signals and systems, which is far higher than that encountered in physical systems or observations. Benign diseases often mimic the features of malignant diseases; malignancies may exhibit a characteristic pattern, which, however, is not always guaranteed to appear. Handling all of the possibilities and degrees of freedom in a biomedical system is a major challenge in most applications. Techniques proven to work well with a certain system or set of signals may not work in another seemingly similar situation. This book intends to provide an insight into the above mentioned applications. Intended Readership The book is directed at engineering students in their nal year of undergraduate studies or in their graduate studies. Most undergraduate students majoring in biomedical engineering are faced with a decision, early in their program of study, regarding the eld in which they would like to specialize. Each chosen specialty has a specic set of course requirements and is supplemented by wise selection of elective and supporting coursework. Also, many young students of biomedical engineering use independent research projects as a source of inspiration and preparation but have difculty identifying research areas that are right for them. Therefore, a second goal of this book is to link knowledge of basic science and engineering to elds of specialization and current research. Practicing engineers, computer scientists, information technologists, medical physicists, and data processing specialists working in diverse areas such as medical, bio signals, biomedical applications, and hospital information systems may nd the book useful in their quest to learn advanced techniques for signal analysis. They could draw inspiration from other applications of signal processing or analysis, and satisfy their curiosity regarding computer applications in medicine and computer aided medical diagnosis. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to Bio medical engineering techniques and applications. It is simultaneously a monograph because it presents several new results and ideas and further developments and explanation of existing algorithms which are brought together and published in the book for the rst time. Furthermore, the research results previously scattered in many scientic journals and conference papers worldwide, are methodically collected and presented in the book in a unied form. As a result of its twofold character the book is likely to be of interest to graduate and postgraduate students, engineers and scientists working in the eld of biomedical engineering, communications, electronics, computer science, optimization, and neural networks. Furthermore, the book may also be of interest to researchers working in VII different areas of science, because a number of results and concepts have been included which may be advantageous for their further research. One can read this book through sequentially but it is not necessary since each chapter is essentially self-contained, with as few cross references as possible. So, browsing is encouraged. The editor would like to thank the authors, who have committed so much effort to the publication of this work. Dr Ganesh R Naik RMIT University, Melbourne, Australia ganesh.naik@rmit.edu.au IX Contents Preface V 1. Micro Macro Neural Network to Recognize Slow Movement: EMG based Accurate and Quick Rollover Recognition 1 Takeshi Ando, Jun Okamoto and Masakatsu G. Fujie 2. Compression of Surface Electromyographic Signals Using Two-Dimensional Techniques 17 Marcus V. C. Costa, João L. A. Carvalho, Pedro A. Berger, Adson F. da Rocha and Francisco A. O. Nascimento 3. A New Method for Quantitative Evaluation of Neurological Disorders based on EMG signals 39 Jongho Lee, Yasuhiro Kagamihara and Shinji Kakei 4. Source Separation and Identication issues in bio signals: A solution using Blind source separation 53 Ganesh R Naik and Dinesh K Kumar 5. Sources of bias in synchronization measures and how to minimize their effects on the estimation of synchronicity: Application to the uterine electromyogram 73 Terrien Jérémy, Marque Catherine, Germain Guy and Karlsson Brynjar 6. Multichannel analysis of EEG signal applied to sleep stage classication 101 Zhovna Inna and Shallom Ilan 7. P300-Based Speller Brain-Computer Interface 137 Reza Fazel-Rezai 8. Alterations in Sleep Electroencephalography and Heart Rate Variability During the Obstructive Sleep Apnoea and Hypopnoea 149 Dean Cvetkovic, Haslaile Abdullah, Elif Derya Übeyli, Gerard Holland and Irena Cosic 9. Flexible implantable thin lm neural electrodes 165 Sami Myllymaa, Katja Myllymaa and Reijo Lappalainen 10. Developments in Time-Frequency Analysis of Biomedical Signals and Images Using a Generalized Fourier Synthesis 191 Robert A. Brown, M. Louis Lauzon and Richard Frayne X 11. Automatic Counting of Aedes aegypti Eggs in Images of Ovitraps 211 Carlos A.B. Mello, Wellington P. dos Santos, Marco A.B. Rodrigues, Ana Lúcia B. Candeias, Cristine M.G. Gusmão and Nara M. Portela 12. Hyperspectral Imaging: a New Modality in Surgery 223 Hamed Akbari and Yukio Kosugi 13. Dialectical Classication of MR Images for the Evaluation of Alzheimer’s Disease 241 Wellington Pinheiro dos Santos, Francisco Marcos de Assis, Ricardo Emmanuel de Souza and Plínio Bezerra dos Santos Filho 14. 3-D MRI and DT-MRI Content-adaptive Finite Element Head Model Generation for Bioelectomagnetic Imaging 251 Tae-Seong Kim and Won Hee Lee 15. Denoising of Fluorescence Confocal Microscopy Images with Photobleaching compensation in a Bayesian framework 275 Isabel Rodrigues and João Sanches 16. Advantages of virtual reality technology in rehabilitation of people with neuromuscular disorders 301 Imre CIKAJLO and Zlatko MATJAČIĆ 17. A prototype device to measure and supervise urine output of critical patients 321 A. Otero, B. Panigrahi, F. Palacios, T. Akinev, and R. Fernández 18. Wideband Technology for Medical Detection and Monitoring 335 Mehmet Rasit Yuce, Tharaka N. Dissanayake and Ho Chee Keong 19. “Hybrid-PLEMO”, Rehabilitation system for upper limbs with Active / Passive Force Feedback mode 361 Takehito Kikuchi and Junji Furusho 20. Fractional-Order Models for the Input Impedance of the Respiratory System 377 Clara Ionescu, Robin De Keyser, Kristine Desager and Eric Derom 21. Modelling of Oscillometric Blood Pressure Monitor – from white to black box models 397 Eduardo Pinheiro and Octavian Postolache 22. Arterial Blood Velocity Measurement by Portable Wireless System for Healthcare Evaluation: The related effects and signicant reference data 413 Azran Azhim and Yohsuke Kinouchi 23. Studying Ion Channel Dysfunction and Arrythmogenesis in the Human Atrium: A Computational Approach 433 Sanjay R. Kharche, Phillip R. Law, and Henggui Zhang 24. Discovery of Biorhythmic Stories behind Daily Vital Signs and Its Application 453 Wenxi Chen [...]... EMG) MMNN (Input: 10 (msec) raw EMG and 40 ARV EMG among 40 (msec)) Only Micro part in MMNN (Input: 10 (msec) raw EMG) Only Macro part in MMNN (Input: 40 ARV EMG among 40 (msec)) tresponse msec -25 (S.D 59) Nfalse 15 0/360 -65 (S.D 55) 51/ 360 -50 (S.D 26) 210 /360 1 (S.D 55) 56/360 Table 2 Features of TDNN, MMNN, and Micro and Macro parts of MMNN 12 Recent Advances in Biomedical Engineering (a) Input signal... defined in our network as the Micro Part The input data, micro x1 in the Micro Part is defined as following; n micro x1 = semg (t –n +1) n (5) where n = 1, 2, ,Nmicro, and Nmicro is the number of input unit in Micro part As can be seen in Fig 5, the data for -Tmicro < t < 0 is the Micro Part, and the data for –(Tmacro + Tmicro ) < t < -Tmicro is the Macro Part In addition, the input data, macro x1 in. .. Cybern., pp .11 13 -11 15, Cambridge, USA, November 19 89 Helen J Hislop & Jacqueline Montgomery (2002) Daniels and Worthingham's Muscle Testing: Techniques of Manual Examination, W B Saunders Co; 7th, 97807 216 92999, pp. 51- 55, 2002 Yanfeng Hou; Zurada, J.M & Karwowski W (2004) Prediction of EMG signals of trunk muscles in manual lifting using a neural network model, Proceedings of 2004 IEEE International Joint... human shoulder joint motion assist, IEEE/ASME Transactions on Mechatronics, 8 (1) , 2003, pp .12 5 -13 5, 10 83-4435 Kumar S.; Chaffin D & Redfern M (19 89) EMG of trunk muscles in isometric and isokinetic MVC, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Images of the Twenty-First Century, vol.3, pp .10 2 910 30, Seattle, USA, November 19 89 Micro Macro... following equation: N macro Tmacro T ARV (7) where Nmacro is the number of input units of the Macro Part The relations between each pair of units in both the Macro Part and the Micro Part are shown in (1) , (2), and (3) above The output data of the Micro part and Macro part is defined as the input data of the Integrated Layer In the Integrated layer, the output signal is calculated using also (1) , (2)... x1 in the Macro Part is n divided into several TARV (msec), and the average rectified value (ARV) of the EMG signal among the TARV values, calculated by (6), is defined as the input value of the Macro Part 6 Recent Advances in Biomedical Engineering t nTARV macro 1 n x semg (i) i t ( n 1) TARV (6) TARV where n = 1, 2, ,Nmacro Therefore, the number of input units of the Macro Part is expressed... with Huffman encoders in several biomedical signal compression algorithms 22 Recent Advances in Biomedical Engineering Fig 4 Step-by-step arithmetic encoding process for the message “BACADEA”, with probability model: p(A)=3/7, p(B) =1/ 7, p(C) =1/ 7, p(D) =1/ 7 and p(E) =1/ 7 Fig 5 Differential encoding example: (a) original signal; (b) encoded signal 3.2 Lossy compression There are two main categories of lossy... Signals Using Two-Dimensional Techniques 17 2 X Compression of Surface Electromyographic Signals Using Two-Dimensional Techniques Marcus V C Costa1, João L A Carvalho1, Pedro A Berger2, Adson F da Rocha1 and Francisco A O Nascimento1 1Department of Electrical Engineering of Computer Science University of Brasília Brazil 2Department 1 Introduction Surface electromyographic (S-EMG) signals provide non-invasive... preserving the morphological characteristics of the waveform In theory, signal compression is the process where the redundant information contained in the signal is detected and eliminated Shannon (19 48) defined redundancy as one minus “the ratio of the entropy of a source to the maximum value it could have while still restricted to the same symbols” 20 Recent Advances in Biomedical Engineering Signal... Orthoses, Proceedings of 2007 IEEE/ICME International Conference on Complex Medical Engineering, 12 83 -12 87, Beijing, China, June 2007 Hayashi, T.; Kawamoto, H.; Sankai, Y.(2005) Control method of robot suit HAL working as operator's muscle using biological and dynamical information, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 3063 – 3068, 07803-8 912 -3, Edmonton, . I Recent Advances in Biomedical Engineering Recent Advances in Biomedical Engineering Edited by Dr Ganesh R Naik In- Tech intechweb.org Published by In- Teh In- Teh Olajnica 19 /2, 32000. specialization including bioinstrumentation, bioimaging, biomechanics, biomaterials, and biomolecular engineering. Biomedical engineering covers recent advances in the growing eld of biomedical. units in the TDNN are shown in (1) , (2), and (3). m i n j m j m ij m i θxωnet m 1 1 1 (1) )( m i m i netfx (2) ))exp (1( 1)( 0 netunetf (3) Recent Advances in Biomedical