Adaptive, autoregressive spectral estimation for analysis of electrical signals of gastric origin

The electrical activity of the human stomach, which normally shows a frequency of about 0.05 Hz, may be studied non-invasively by either cutaneous electrogastrography (EGG) or surface magnetogastrography (MGG). Detection of changes in frequency with time may be useful to characterize gastric disorders. The fast Fourier transform (FFT) has been the most commonly used method for the automated spectral analysis of the signals obtained from the EGG or the MGG. We have used an autoregressive (AR) parametric spectrum estimator to analyse simulated signals of gastric electrical activity, and to evaluate the results of human studies using EGG and MGG. In comparison with the FFT, our results showed that the AR spectrum estimator provided more detailed qualitative information about frequency variations of short duration simulated signals than the FFT. In the human studies, the AR estimator was as good as the conventional FFT methods in detecting physiological changes in frequency and in identifying abnormal recordings. We conclude that the AR spectral estimator may provide a better qualitative analysis of frequency variations in small portions of the signal, and is as useful as the FFT to analyse human EGG or MGG studies.