Key result
The block based error normalized RLS algorithm improved the average signal-to-noise ratio to 7.7363 dB compared to 4.4950 dB with the LMS algorithm for ECG muscle artifact reduction.
Absolute Event Rate: 7.7363% vs 4.495%
The proposed block-based error normalized RLS algorithm provides superior noise reduction and faster convergence for ECG artifact removal compared to traditional LMS algorithms.
May enhance ECG denoising in simulations; leaves open clinical validation before practice change.
When we acquiring the Electrocardiogram (ECG) signal fro m the person, the signal amplitude (PQRST) and timing values are changes due to various artefacts. The different artefacts are Baseline wander, power line interference, muscle artefact, mot ion artefact and the channel noise also added sometimes during the transmission of the signal for diagnosis purpose. The adaptive filters play vital ro le fo r reduction of noise in the desired signals. In this paper we proposed, block based error normalized Recursive Least Square (RLS) adaptive algorith m and sign based RLS adaptive algorith m, wh ich are used for reduction of muscle artifact noise and base line wander noise in the ECG signal. Fro m the simulat ion result we analy zed that, co mparing to Least Mean Square algorith m, the proposed RLS algorith m gives fast convergence rate with high signal to noise ratio and less mean square error.
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Gowri et al. (2016) studied ECG signal artifacts (n=47). Block based error normalized RLS (BB-ENRLS) algorithm vs. Least Mean Square (LMS) algorithm was evaluated on Average Signal to Noise Ratio (SNR) for Muscle Artifact reduction. The block based error normalized RLS algorithm improved the average signal-to-noise ratio to 7.7363 dB compared to 4.4950 dB with the LMS algorithm for ECG muscle artifact reduction.
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