HERMITE EXPANSIONS OF COMPACT SUPPORT WAVE-FORMS - APPLICATIONS TO MYOELECTRIC SIGNALS

被引:83
作者
LOCONTE, LR
MERLETTI, R
SANDRI, GV
机构
[1] BOSTON UNIV, DEPT BIOMED ENGN, BOSTON, MA 02215 USA
[2] BOSTON UNIV, NEUROMUSCULAR RES CTR, BOSTON, MA 02215 USA
[3] POLITECN TORINO, DIPARTIMENTO ELETTRON, I-10129 TURIN, ITALY
[4] BOSTON UNIV, DEPT AEROSP & MECH ENGN, BOSTON, MA 02215 USA
关键词
D O I
10.1109/10.335863
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Nonstationary signals with finite time support are frequently encountered in electrophysiology and other fields of biomedical research. It is often desirable to have a compact description of their shape and of their time evolution. For this purpose, Fourier analysis is not necessarily the best tool. The Hermite-Rodriguez and Associated Hermite basis functions are applied in this work. Both are based On the product of Hermite polynomials and Gaussian functions. Their general properties relevant to biomedical signal processing are reviewed. Preliminary applications are described concerning the analysis and description of: a) test signals such as a square pulse and a single cycle of a sinewave, b) electrically evoked myoelectric signals, and c) power spectra of either voluntary or evoked signals. It is shown that expansions with only five to ten terms provide an excellent description of the computer simulated and real signals. It is shown that these two families of Hermite functions are well suited for the analysis of nonstationary biological evoked potentials with compact time support. An application to the estimation of scaling factors of electrically evoked myoelectric signals is described. The Hermite functions show advantages with respect to the more traditional spectral analysis, expecially in the case of signal truncation due to stimulation with interpulse intervals smaller than the duration of the evoked response. Finally, the Hermite approach is found to be suitable for classification of spectral shapes and compression of spectral information of either voluntary or evoked signals. The approach is very promising for neuromuscular diagnosis and assessment because of its capability for information compression and waveform classification.
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页码:1147 / 1159
页数:13
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