Dual phase dependent RLS filtering approach for baseline wander removal in ECG signal acquisition

Abstract

Electrocardiogram (ECG) signals often get contaminated during physical activity, due to baseline disturbances. Removal of baseline interference also leads to variation in the timing of ECG waves. This paper proposes a dual phase dependent-recursive least square (DPD-RLS) adaptive filter that removes the baseline interference during ECG signal acquisition. This approach initially detects the upper envelope followed by the subtraction of the upper envelope with the original signal. The lower envelope is then detected from the resultant signal. Two phases are extracted from the upper envelope signal and the lower envelope signal that compensates for the phase change effects during the baseline wander removal process. A DPD-RLS adaptive filter structure is proposed that compensates for the phase changes near the P, Q, and T onset of the ECG signal. The evaluation was done using the clean ECG signals from the MIT-BIH arrhythmia dataset and baseline wander signals from MIT-BIH noise stress dataset signals using the metrics such as output signal to noise ratio (SNRo), correlation coefficient (γ), and percent root mean square difference (PRD) for the different input signal to noise ratios (SNRi). The proposed scheme provides an average output SNR, the correlation coefficient of 14.07dB and 0.980 respectively at SNRi=5dB when evaluated on different ECG records. Experimental results reveal that the proposed baseline wander removal scheme outperforms the traditional schemes.