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The frequency content of many biomedical signals can change rapidly with time. Conventional Fourier spectral analysis techniques are insufficient for analyzing the time-varying spectral content of these signals. By mapping a one-dimensional function of time (or frequency), the time-frequency representation can localize the signal energy in both the time and frequency directions. It has been shown that many biomedical signal problems may benefit from time-frequency analysis. The objective of this paper is to review the advances in time-frequency analysis of biomedical signals. Relevant theoretical methodologies and practical considerations are introduced, and five application areas are reviewed: electroencephalography (EEG), electrocardiography, phonocardiography, electrogastrography, and electromyography.

Adaptive cancellation of motion artifacts in the electrogastrogram (EGG) is presented in this paper. The EGG is a surface measurement of gastric electrical activity. Like other noninvasive electrophysiological measurements, the EGG contains motion artifacts. A number of papers have been published on the adaptive cancellation of motion artifacts or interferences in biomedical signals. Adaptive filtering was performed in time domain in almost all of the previous publications. In this paper, however, three different sorts of adaptive filters were investigated and their efficiencies in cancellation of motion artifacts were compared with each other. These include time-domain, transform-domain, and frequency-domain adaptive filters. A series of simulations were conducted to investigate the performance of these adaptive filters in cancellation of respiratory and motion artifacts. The results show that the frequency-domain adaptive filter has superior performance over the time- and transform-domain adaptive filters in the cancellation of stationary respiratory artifacts in the EGG. Although results focus on the EGG, this paper provides useful information for adaptive filtering of other biomedical signals.

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