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In recent years there has been rapid progress in the development of signal processing in general, and more specifically in the application of signal processing and pattern analysis to biological signals. Techniques, such as parametric and nonparametric spectral estimation, higher order spectral estimation, time-frequency methods, wavelet transform, and identifi cation of nonlinear systems using chaos theory, have been successfully used to elucidate basic mechanisms of physiological and mental processes. Similarly, biological signals recorded during daily medical practice for clinical diagnostic procedures, such as electroen cephalograms (EEG), evoked potentials (EP), electromyograms (EMG) and electrocardio grams (ECG), have greatly benefitted from advances in signal processing. In order to update researchers, graduate students, and clinicians, on the latest developments in the field, an International Symposium on Processing and Pattern Analysis of Biological Signals was held at the Technion-Israel Institute of Technology, during March 1995. This book contains 27 papers delivered during the symposium. The book follows the five sessions of the symposium. The first section, Processing and Pattern Analysis of Normal and Pathological EEG, accounts for some of the latest developments in the area of EEG processing, namely: time varying parametric modeling; non-linear dynamic modeling of the EEG using chaos theory; Markov analysis; delay estimation using adaptive least-squares filtering; and applications to the analysis of epileptic EEG, EEG recorded from psychiatric patients, and sleep EEG.
Im Mittelpunkt dieses modernen und spezialisierten Bandes stehen adaptive Strukturen und unüberwachte Lernalgorithmen, besonders im Hinblick auf effektive Computersimulationsprogramme. Anschauliche Illustrationen und viele Beispiele sowie eine interaktive CD-ROM ergänzen den Text.
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.
Generally speaking, Biosignals refer to signals recorded from the human body. They can be either electrical (e. g. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), etc. ) or non-electrical (e. g. breathing, movements, etc. ). The acquisition and processing of such signals play an important role in clinical routines. They are usually considered as major indicators which provide clinicians and physicians with useful information during diagnostic and monitoring processes. In some applications, the purpose is not necessarily medical. It may also be industrial. For instance, a real-time EEG system analysis can be used to control and analyze the vigilance of a car driver. In this case, the purpose of such a system basically consists of preventing crash risks. Furthermore, in certain other appli- tions,asetof biosignals (e. g. ECG,respiratorysignal,EEG,etc. ) can be used toc- trol or analyze human emotions. This is the case of the famous polygraph system, also known as the “lie detector”, the ef ciency of which remains open to debate! Thus when one is dealing with biosignals, special attention must be given to their acquisition, their analysis and their processing capabilities which constitute the nal stage preceding the clinical diagnosis. Naturally, the diagnosis is based on the information provided by the processing system.
The analysis of bioelectrical signals continues to receive wide attention in research as well as commercially because novel signal processing techniques have helped to uncover valuable information for improved diagnosis and therapy. This book takes a unique problem-driven approach to biomedical signal processing by considering a wide range of problems in cardiac and neurological applications-the two "heavyweight" areas of biomedical signal processing. The interdisciplinary nature of the topic is reflected in how the text interweaves physiological issues with related methodological considerations. Bioelectrical Signal Processing is suitable for a final year undergraduate or graduate course as well as for use as an authoritative reference for practicing engineers, physicians, and researchers. A problem-driven, interdisciplinary presentation of biomedical signal processing Focus on methods for processing of bioelectrical signals (ECG, EEG, evoked potentials, EMG) Covers both classical and recent signal processing techniques Emphasis on model-based statistical signal processing Comprehensive exercises and illustrations Extensive bibliography