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This dissertation, "Fast Signal Processing Techniques for Surface Somatosensory Evoked Potentials Measurement" by Shing-chun, Benny, Lam, 林成俊, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled FAST SIGNAL PROCESSING TECHNIQUES FOR SURFACE SOMATOSENSORY EVOKED POTENTIALS MEASUREMENT Submitted by Shing Chun Benny LAM for the degree of Master of Philosophy at The University of Hong Kong in August 2003 Somatosensory evoked potential (SEP) testing has been widely applied to intraoperative spinal cord integrity monitoring, diagnosis of various neurological disorders, and nerve conduction velocity measurements. However, the SEP recorded using surface electrodes is buried in noises that are both electrical and biological in nature. Hence, the noninvasive measurements of these potentials suffer from very poor signal-to-noise ratios (SNR). Some means of signal processing is required to extract SEP signal from strong background noise. The most commonly used technique of SEP extraction is ensemble averaging (EA). The conventional EA method usually requires several hundred to thousands of raw SEP input trials to produce an identifiable waveform for latency and amplitude measurement. This is time-consuming and may lead to the failure to detect the dynamic behaviour of the evoked potentials. Therefore, a fast and accurate SEP extraction technique is needed to reduce the measurement time. An adaptive Signal Enhancer (ASE) was applied to extract the weak and noisy SEP signal from anesthetized subjects during surgery. ASE has a self-learning ability in that the weights of the ASE are able to adjust according to each input trial. This ability makes the ASE suitable to track the noisy and time-varying SEP in fewer input trials than EA. The best estimation of the SEP signal can be obtained upon the i convergence of the ASE. ASE with 50 input trials provided results comparable to those extracted by conventional EA. In order to examine the ability of ASE in detecting SEP during spinal cord compression, an animal study simulating different level of spinal cord compression was conducted. ASE with 50 input trials successfully detected the SEP during the normal situation and spinal cord. During neurological diagnosis of conscious subjects, noises are much more complicated and severe than those in anesthetized subjects. On-going electroencephalography, electromyography, visual evoked potentials, and brainstem auditory evoked potentials continuously add to the surface SEP waveform during the data acquisition process. ASE was insufficient to extract an SEP for latency and amplitude measurement. A Multi-Adaptive Filtering (MAF) technique was developed for this purpose. This technique is a combination of an Adaptive Noise Canceller (ANC) and the ASE in which the raw surface recorded SEP is first processed by ANC with a reference noise channel of background noise for adaptive subtraction before entering ASE. The purpose of the ANC is to eliminate the correlated noises so that the SNR is increased before ASE processing. The MAF was theoretically developed and verified by filtering simulated SEP signals in which electroencephalography and Gaussian noise of different SNRs were added. The technique was also applied to track surface SEP recorded from conscious human subjects. It was found that the MAF provided similar SEP detection to the conventional averaging method in much less data acquisition time. Efficient and effective surface SEP measurement for neurological diagnosis is beneficial to clinicians and patients. ii DOI: 10.5353/th_b2924640 Subjec
Adaptive filtering is useful in any application where the signals or the modeled system vary over time. The configuration of the system and, in particular, the position where the adaptive processor is placed generate different areas or application fields such as: prediction, system identification and modeling, equalization, cancellation of interference, etc. which are very important in many disciplines such as control systems, communications, signal processing, acoustics, voice, sound and image, etc. The book consists of noise and echo cancellation, medical applications, communications systems and others hardly joined by their heterogeneity. Each application is a case study with rigor that shows weakness/strength of the method used, assesses its suitability and suggests new forms and areas of use. The problems are becoming increasingly complex and applications must be adapted to solve them. The adaptive filters have proven to be useful in these environments of multiple input/output, variant-time behaviors, and long and complex transfer functions effectively, but fundamentally they still have to evolve. This book is a demonstration of this and a small illustration of everything that is to come.
Evoked potentials have been used for decades to assess neurologic function in outpatient studies and are now routinely used in the operating room during surgery. Illustrated Manual of Clinical Evoked Potentials is a modern, practical guide to performing these studies and interpreting the results. The book is uniquely organized as a singular resource that provides the necessary background for understanding and conducting evoked potential studies. It functions as a multi-purpose text, atlas, and reading session, with numerous examples of studies and findings and discussion of key takeaways. Divided into five chapters, the book opens with an introduction to the basics of data acquisition and interpretation that lays the foundation for the modality-specific chapters that follow. The next group of chapters are in-depth reviews of visual, brainstem auditory, and somatosensory evoked potentials. Each of these chapters lays out the specifics of the modality and study protocol with examples to show how things should—and should not—be done. Sample studies with discussions about how to interpret them highlight a particular aspect of normalcy or pathology. Imaging correlates are provided to emphasize salient points and offer perspective. The final chapter is an overview of the use of evoked potentials during surgery with imaging and case discussions to introduce the reader to this very important application. Key Features Detailed review of methodology of evoked potential studies Many examples of actual patient studies with imaging correlates Interpretation of each evoked potential study presented in detail “Reading session”-like discussion of each example Special chapter on evoked potentials in the operating room
Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. Multiple color illustrations are integrated in the text Includes an introduction to biomedical signals, noise characteristics, and recording techniques Basics and background for more advanced topics can be found in extensive notes and appendices A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670
Brain dysfunction is a major clinical problem in intensive care, with potentially debilitating long-term consequences for post-ICU patients of any age. The resulting extended length of stay in the ICU and post-discharge cognitive dysfunction are now recognized as major healthcare burdens. This comprehensive clinical text provides intensivists and neurologists with a practical review of the pathophysiology of brain dysfunction and a thorough account of the diagnostic and therapeutic options available. Initial sections review the epidemiology, outcomes, relevant behavioral neurology and biological mechanisms of brain dysfunction. Subsequent sections evaluate the available diagnostic options and preventative and therapeutic interventions, with a final section on clinical encephalopathy syndromes encountered in the ICU. Each chapter is rich in illustrations, with an executive summary and a helpful glossary of terms. Brain Disorders in Critical Illness is a seminal reference for all physicians and neuroscientists interested in the care and outcome of severely ill patients.
Clinical Neurophysiology, Third Edition will continue the tradition of the previous two volumes by providing a didactic, yet accessible, presentation of electrophysiology in three sections that is of use to both the clinician and the researcher. The first section describes the analysis of electrophysiological waveforms. Section two describes the various methods and techniques of electrophysiological testing. The third section, although short in appearance, has recommendations of symptom complexes and disease entities using electroencephalography, evoked potentials, and nerve conduction studies.
This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.
This reference text consists of contributed chapters by specialists directly carrying out research and development in this emerging field which joins advanced microelectronics with modern biotechnology. Chapters present novel biotechnology-based microelectronic instruments, such as those used for de