Toward Early Abnormalities Detection on Digestive System: Multi-Features Electrogastrogram (EGG) Signal Classification based on Machine Learning

Abstract

Electrogastrogram (EGG) is one of the bio-signals that can be developed as a tool for early detection of digestive abnormalities. The use of features extraction and machine learning can be applied to accelerate the development of the system detection. In this paper, five features extraction and two classifiers are used as comparative study. The feature extraction includes Mean Absolute Value (MAV), Average Amplitude Change (AAC), Waveform Length (WL), Maximum Fractal Length (MFL), and Root Mean Square (RMS). ANN and SVM were designed as the proposed classifier. There are two classes that are designed for classification, namely Fasting and Postprandial stages. From the experimental results, it was found that the highest accuracy value is acquired when using SVM classifier and used five features extraction. The classification reached 82.3% that showed significant result. From the experimental results, it is found that EGG function as early diseases detection on digestive system is very promising i.e., Covid-19 effect to digestive system.