A New LMS Based Noise Removal and DWT Based R-peak Detection in ECG Signal for Biotelemetry Applications

ECG signals are non-stationary pseudo periodic in nature and whose behavior changes with time. The proper processing of ECG signal and its accurate feature extraction is very much essential since it determines the condition of the heart. Least mean square (LMS) based adaptive filters are widely deployed for removing artifacts in ECGs due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The MSE can be reduced by transform domain variable step size LMS algorithm at the cost of computational complexity. In this paper, a variable step size delayed LMS adaptive filter is used to remove the artifacts from ECG signal for improved feature extraction. Moreover, the extraction of R peak in ECG is carried out using discrete wavelet transform based QRS detection algorithm. Due to the high speed of our method, the ECG de-nosing and R Peak extracting could be both realized at real time, which is an effective method to monitor the patients in biotelemetry applications.