The High-Dimensional Signal Classification of Electrogastrogram for Detection of Gastric Motility Disorders

Abstract

The electrogastrogram (EGG) can detect the gastric electromyogram activity, and then reflect the relative change of the rhythm as well as amplitude of the slow wave of the electromyogram. As EGG has the advantages of convenient, painless, non-invasive and accurate measurement of gastric electromyogram activity, it can not only be used to evaluate the effects of gastromotor drugs and gastrointestinal hormones, but also to distinguish healthy people from functional dyspepsia, patients with gastric cancer and patients with low gastric motility according to the results of parameter analysis in EGG. This paper proposes an EGG signal processing and classification method to realize the potential role of EGG in the diagnosis and management of gastrointestinal diseases. First, EGG signal collection was conducted on normal people and patients, and then the test signal was described as accurately as possible according to some key features of the gastric waveform. Based on the collected data, we developed an indicator that can classify high-dimensional signals and provide an indicator that can distinguish or identify two kinds of signal-related indicators. In this way, EGG signals are associated with specific conditions for clinical diagnosis of gastrointestinal dysrhythmia and even for efficacy evaluation.