Key result
The RLS algorithm demonstrated lower mean square error and better noise cancellation capacity compared to the LMS algorithm for reducing power line interference and AWGN in ECG signals.
Why the study?
ECG signals used in biotelemetry for diagnosing patients in remote locations are commonly recorded with noise, requiring careful analysis to determine patient status and pathophysiology.
LMS and RLS algorithms on adaptive FIR filters can be used to reduce noise in ECG signals for biotelemetry applications.
In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and Mean Square Error.
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Naveen et al. (2019) studied ECG signal noise (n=5). RLS and LMS algorithms on adaptive FIR filter vs. Noisy ECG signals was evaluated on Mean Square Error and Noise Power. The RLS algorithm demonstrated lower mean square error and better noise cancellation capacity compared to the LMS algorithm for reducing power line interference and AWGN in ECG signals.
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