Evolutionary optimization-based descendent adaptive filter for noise confiscation in electrocardiogram signals

Electrocardiogram (ECG) signals are usually contaminated by numerous artefacts during the recording process, and the quality of physiological information related to the heart is compromised. Due to this, artefact cancellation has become necessary for ECG signals. In this paper, swarm intelligence-based optimally tuned adaptive noise cancellers (ANCs) have been proposed and applied to denoise the ECG signal. The results have been analysed both qualitatively and quantitatively for noise cancellation from ECG signals through the ANCs optimized by using the seagull optimization algorithm (SOA), the Neighbourhood-based lineal population size success history-based adaptive differential evolution (NLSHADE) algorithm and the hyperbolic gravitational search algorithm (HGSA). The performance of the proposed methodology has been validated by using the additive white Gaussian noise at a diverse signal-to-noise ratio (SNR) on two publicly available datasets of ECG signal from the arrhythmia database (ADB) and QT ECG database (QTDB). The reference noise for ANC was considered using the noise stress test database (NSTDB). The performance of SOA-assisted ANC has been tested with the help of the Wilcoxon signed-rank test. The proposed technique-based ANCs supplied an enhanced percentage root mean squared deviation (PRD) value of 3.40E-03, mean squared error (MSE) value of 1.35E-11 and mean SNR improvement of 10.986 dB as compared to the reported state-of-the-art methods along with the benchmark competent algorithms, namely NLSHADE and HGSA.