Ecg and Power Line Noise Removal From Respiratory Emg Signal Using Adaptive Filters

Surface electromyography (semg) from respiratory muscles is a non-invasive and effective method of studying neuromuscular diseases, muscle fatigue, enhancement of muscular function and also human-computer interface. this signal is contaminated by different noises. these include environmental noises like power line noise and also internal noises such as electrocardiographic noise. the clean emg signal can be extremely useful for pathological purposes. in this study, diaphragmatic emg signals were recorded with power lab system from seven subjects. the signals showed contamination due to power line interference (pli) and also cardiac activity. adaptive filters were used to reduce cardiac noise as well as 50 hz (the fundamental) power line noise and its harmonics. recursive least squares algorithm was used for the structure of the adaptive filter. different values of the filter parameters; filter order and forgetting factor were examined for the noise removal purpose. performance of the adaptive filter was quantified by signal-to-noise ratio and coherence measures for simulated data. the results show that we can successfully eliminate pli and ecg noise from semg signals with adaptive filters. the figures and tables obtained help to decide which parameters of the filter are the best for our study.