Diagnosis of Type 2 Diabetes Using Electrogastrograms: Extraction and Genetic Algorithm–Based Selection of Informative Features

• Published in: JMIR biomedical engineering Vol. 5; no. 1; p. e20932
• Main Authors: Alagumariappan, Paramasivam, Krishnamurthy, Kamalanand, Kandiah, Sundravadivelu, Cyril, Emmanuel, V, Rajinikanth
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 07.10.2020
• Subjects: Accuracy; Classification; Diabetes; Eggs; Electrodes; Entropy; Fractals; Random variables; Signal processing; Standard deviation; Stomach
• ISSN: 2561-3278; 2561-3278
• DOI: 10.2196/20932

Background: Electrogastrography is a noninvasive electrophysiological procedure used to measure gastric myoelectrical activity. EGG methods have been used to investigate the mechanisms of the human digestive system and as a clinical tool. Abnormalities in gastric myoelectrical activity have been observed in subjects with diabetes. Objective: The objective of this study was to use the electrogastrograms (EGGs) from healthy individuals and subjects with diabetes to identify potentially informative features for the diagnosis of diabetes using EGG signals. Methods: A total of 30 features were extracted from the EGGs of 30 healthy individuals and 30 subjects with diabetes. Of these, 20 potentially informative features were selected using a genetic algorithm–based feature selection process. The selected features were analyzed for further classification of EGG signals from healthy individuals and subjects with diabetes. Results: This study demonstrates that there are distinct variations between the EGG signals recorded from healthy individuals and those from subjects with diabetes. Furthermore, the study reveals that the features Maragos fractal dimension and Hausdorff box-counting fractal dimension have a high degree of correlation with the mobility of EGGs from healthy individuals and subjects with diabetes. Conclusions: Based on the analysis on the extracted features, the selected features are suitable for the design of automated classification systems to identify healthy individuals and subjects with diabetes.