Bioelectrical Signal Processing in Cardiac and Neurological ApplicationsAcademic Press, 2005/07/21 - 688 ページ 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.
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目次
| 1 | |
| 25 | |
Chapter 3 EEG Signal Processing | 55 |
Chapter 4 Evoked Potentials | 181 |
Chapter 5 The Electromyogram | 337 |
Chapter 6 The ElectrocardiogramA Brief Background | 411 |
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action potentials activity adaptive amplitude applications approach arrhythmia artifacts assumed baseline wander basis functions Biol Biomed brain cardiac characterized Clin clinical cluster coefficients components computed continuous-time correlation cut-off frequency data compression defined denotes described discrete-time ectopic beat EEG signal electrical electrocardiogram electrode Electroencephal Electromyography ensemble average equation evoked potentials example Figure Fourier transform Frequency Hz Gaussian heart rate IEEE IEEE Trans impulse response interpolation latency linear LMS algorithm lowpass filter matrix measure method ML estimator morphology motor unit MUAP trains MUAP waveforms muscle neuron Neurophysiol normal observed signal obtained occur optimal output parameters peak performance periodogram power spectrum prediction error Proc properties QRS complex recorded recursion result RR interval sampling rate scaling Section segment sinus spectral analysis surface EMG techniques time-frequency time-varying tion variability variance vector ventricular wave waveform wavelet weight Wiener filter zero