Abstract

Electromyography (EMG) is a valuable biomedical signal used to study the electrical activity of muscles, providing crucial insights into neuromuscular disorders, motor control, and rehabilitation therapies. However, EMG signals are inherently contaminated with noise and artifacts, challenging the accurate extraction of relevant information. This paper presents a novel approach for enhancing EMG signals using an Infinite Impulse Response (IIR) filter. The IIR filter design was carefully tailored to meet the specific requirements of EMG signal processing, including the extraction of electromyographic information within a specific frequency range while effectively attenuating noise and interference from external sources. The design process involved selecting appropriate filter specifications, such as the cut-off frequencies and filter order, to optimize the trade-off between signal distortion and noise suppression. To validate the effectiveness of the IIR filter, extensive experiments were conducted using both synthetic and real-world EMG signal datasets. The filter performance was compared against conventional filtering techniques, demonstrating superior noise reduction capabilities while preserving essential EMG signal features. Results showed that the proposed IIR filter significantly improved the signal-to-noise ratio and increased the accuracy of subsequent signal analysis algorithms, thereby enhancing the reliability and diagnostic value of EMG data. The filter real-time implementation was also assessed, and its computational efficiency deemed suitable for practical applications.

Authors

K. Aparna¹, A.Vasantharaj², Sudipta Ghosh³, Bhumika Choksi⁴
JNTUA College of Engineering, India¹, Excel Engineering College, India², Calcutta Institute of Engineering and Management, India³, VIT Bhopal University, India⁴

Keywords

Electromyography (EMG), Signal Enhancement, IIR Filter, Signal Distortion