Development of a machine learning model for the classification of healthy and diabetic subjects using electromyography signal

Abstract

Diabetes can lead to complications like Diabetic Peripheral Neuropathy (DPN), which impacts muscle and nerve function. Electromyography (EMG) is a standard diagnostic tool for detecting DPN, but its complex signals make analysis time-consuming, delaying detection and treatment. This study aims to develop and compare machine learning models for classifying healthy and diabetic individuals using EMG data collected during dorsiflexion movement. The Muscle Sensor V3 recorded EMG signals, which were then transformed into time-domain features—Root Mean Square (RMS), Mean Absolute Value (MAV), Standard Deviation (SD), and Variance (VAR)—for classification purposes. Machine learning models, including K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were optimized using Particle Swarm Optimization (PSO). The analysis revealed that healthy individuals exhibited higher EMG amplitudes than those with diabetes. Among the models, ANN achieved the highest classification accuracy (94.44%) compared to SVM (88.89%) and KNN (77.78%). These results demonstrate the effectiveness of ANN as a reliable classifier for distinguishing between healthy and diabetic individuals, offering a more efficient and accurate approach to EMG data analysis for potential clinical applications.