EMG-Spectrogram-Empowered CNN Stroke-Classifier Model Development

Stroke is a leading cause of death and long-term disability worldwide, with ischemic stroke accounting for approximately 62.4% of all cases. This condition often results in persistent motor dysfunction, significantly reducing patients' productivity. The effectiveness of rehabilitation therapy is crucial for post-stroke motor recovery. However, limited access to rehabilitation services particularly in low- and middle-income countries remains a major barrier due to a shortage of experienced professionals. This challenge also affects home-based rehabilitation, an alternative to conventional therapy, which primarily relies on standard evaluation methods that are heavily dependent on expert interpretation. Electromyography (EMG) offers an objective and alternative approach to assessing muscle activity during stroke therapy in home environments. Recent advancements in deep learning (DL) have opened new avenues for automating the classification of EMG data, enabling differentiation between post-stroke patients and healthy individuals. This study introduces a novel methodology for transforming EMG signals into time-frequency representation (TFR) spectrograms, which serve as input for a convolutional neural network (CNN) model. The proposed Tri-CCNN model achieved the highest classification accuracy of 93.33%, outperforming both the Shallow CNN and the classic LeNet-5 architecture. Furthermore, an in-depth analysis of spectrogram amplitude distributions revealed distinct patterns in stroke patients, demonstrating the method's potential for objective stroke assessment. These findings suggest that the proposed approach could serve as an effective tool for enhancing stroke classification and rehabilitation procedures, with significant implications for automating rehabilitation monitoring in home-based rehabilitation (HBR) settings.