Abstract

Filtering the noise present in ECG signal by using adaptive algorithms is the aim of the study. The electrocardiogram (ECG) is the recording of the electrical potential of heart beat. A stationary noise that is commonly found to disturb the basic electrocardiogram is power line interference. It is essential to reduce such disturbance in order to improve accuracy and reliability. The denoising of electrocardiogram signals is a challenging fact as it is difficult to apply filters with fixed coefficients. Adaptive filtering techniques can be used in which the filter coefficients can be varied to track the dynamic variations of the signal. The model is based on combined approach of Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation filter (ANC). A new model is constructed using DWT for reference signal. Denoising is performed by applying ECG signal obtained from MIT-BIH arrhythmia database and the modelled reference signal using LMS and SSLMS filters. The results show that the new model demonstrates an improved performance with respect to the recovery of true ECG signals and also has a better tracking performance.

Keywords:

Adaptive Noise Cancellation (ANC), Electrocardiogram (ECG), Least Mean Square (LMS), Sign Sign LMS (SSLMS),

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.