Gastric myoelectrical signal analysis. A comparative study of different spectral analysis methods

Surface electrogastrogram (EGG) is a promising procedure to analyze gastric myoclectrical activity. Both time and frequency domain analysis methods have been explored to extract significant information from the noisy EGG so that they could be applied to clinical practice. Outperforming FFT analysis, the recently developed spectral analysis based on the adaptive line enhancer (ALE) has been shown reliable to detect dysrhythmic events transiently embedded in gastric myoelectrical potentials. This study is to introduce a different procedure that is based on the lattice structure, and compare it with FFT. and ALE-based methods for detecting episodic rhythmic variations in the nonstationary EGG. In particular, the adaptive learning property and spectrum resolution of the method are discussed, and its applicability to EGG analysis explored. Using a stochastic approximation, the filter parameters are updated and the system can track the input frequency change, giving a spectrum every T seconds that represents rhythmic variations in the EGG being analyzed. It is concluded that the proposed algorithm has a shorter convergence time and results in higher spectrum resolution as compared to the other methods. Experimental results with simulated and real data are provided to show the efficacy and robustness of the proposed method.