Data-Driven Stroke Classification Utilizing Electromyographic Muscle Features and Machine Learning Techniques

Abstract

Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models. Methods: EMG data were collected from the legs of 120 healthy individuals and 120 stroke patients during gait. Four statistical features were extracted from 16 EMG signals and trained on seven ML-based models. The accuracy of the validation and test datasets was also examined. Results: The model with the best performance was Random Forest. Among the 16 EMG signals, the average and maximum values of the muscle activities involved in knee extension (i.e., vastus medialis and rectus femoris) contributed significantly to the predictions. Conclusion: The results of this study confirmed that the simple processing and feature extraction of EMG signals effectively contributed to the accuracy of ML-based models. Routine use of EMG data collected in clinical environments is expected to provide benefits in terms of stroke prevention and rehabilitation.