Advancing Electromyography Signal Analysis: Wavelet-Based Filtering for Low Back Pain Patients Assessment

Low back pain (LBP) is a prevalent condition that significantly impacts daily activities. This study examines statistical differences in surface electromyography (sEMG) signals between healthy individuals and LBP patients during the contraction phase of the Seated Side Bend (SB) exercise. We employed conventional filtering (CF) and Discrete Wavelet Transform (DWT) with 'sym5' and 'db4' wavelets to extract time-domain and time-frequency domain features. Statistical analysis using t-tests identified significant differences in muscle activity between the groups. Our results demonstrated that wavelet-based processing, particularly using the db4 and sym5 wavelets, outperforms conventional filtering in analyzing muscle activation during the SB exercise, with conventional filtering showing inconsistency in noise removal. These findings highlight the superior effectiveness of wavelet-based EMG signal processing in assessing muscle activity in LBP patients and have potential applications in biomedical research, clinical diagnostics, and healthcare.