Logo image
Time-frequency methods for detecting spike activity of stomach
Journal article   Peer reviewed

Time-frequency methods for detecting spike activity of stomach

A Akin and H H Sun
Medical & biological engineering & computing, v 37(3), pp 381-390
May 1999
PMID: 10505391

Abstract

Animals Dogs Electrophysiology Gastrointestinal Motility Signal Processing, Computer-Assisted Stomach - physiology
It has been hypothesised by many researchers that the spike activity signals of the stomach are responsible for triggering peristaltic contractions. Since most gastric motility disorders include an abnormality in the contraction pattern, it is very important to access this information non-invasively. The aim in this study is to use abdominal electrogastrogram (EGG) signals to detect the spike activity signals generated by the serosa of the stomach, and hence provide clinicians with a better method to monitor the motility state of the stomach. Through second and third-order spectral estimations performed on the serosal data obtained from canine experiments, it was concluded that the spike activity in serosal signals occupies a frequency range of 50-80 cycles per minute. An increase in this frequency range during strong antral contractions was observed both in the serosal and cutaneous power spectra. By using the 'continuous wavelet transform' with respect to a modified Morlet wavelet, the spike activity signals generated from the serosa of the stomach can be detected and quantified in time from the cutaneous EGG records. During phase III contraction episodes, a detection accuracy of up to 96% from the cutaneous EGG recordings was calculated based on the scored serosal spike activities simultaneously recorded.

Metrics

7 Record Views
19 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Mathematical & Computational Biology
Medical Informatics
Logo image