Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms.

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3 dB and 6.8 dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3 dB and 0.6 dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.