Optimized overcomplete signal representation and its applications to time-frequency analysis of electrogastrogram.

The electrogastrogram (EGG) is a surface measurement of gastric myoelectrical activity. The normal frequency of gastric myoelectrical activity in humans is 3 cycles/min. Abnormal frequencies in gastric myoelectrical activity have been found to be associated with functional disorders of the stomach. The aim of this article was, therefore, to develop new time-frequency analysis methods for the detection of gastric dysrhythmia from the EGG. A concept of overcomplete signal representation was used. Two algorithms were proposed for the optimization of the overcomplete signal representation. One was a fast algorithm of matching pursuit and the other was based on an evolutionary program. Computer simulations were performed to compare the performance of the proposed methods in comparison with existing time-frequency analysis methods. It was found that the proposed algorithms provide higher frequency resolution than the short time Fourier transform and Wigner-Ville distribution methods. The practical application of the developed methods to the EGG is also presented. It was concluded that these methods are well suited for the time-frequency analysis of the EGG and may also be applicable to the time-frequency analysis of other biomedical signals.