Electrocardiogram signal filtering using circulant singular spectrum analysis and cascaded Savitzky-Golay filter. (May 2022)

• Record Type: Journal Article
• Authors: Krishna Chaitanya, M.; Sharma, Lakhan Dev
• Is Part Of: Biomedical signal processing and control. Volume 75(2022)
• Publication Date: 2022-05
• Subjects: Powerline interference -- Baseline wander -- Circulant singular spectrum analysis -- Savitzky-Golay filter -- ECG signal; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103583
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Newly developed CiSSA is used for baseline wander elimination in ECG signals. PLI in ECG signals is expunged by using four stage cascaded SG filter. Exhaustive analyses are done on all the records of MITBIHDB. This method removes the combination of BW and PLI noises from the ECG signals. Achieved an average SNRout of 24.48 and CC of 0.99. Abstract: The electrocardiogram (ECG) is a tool that is used to examine the heart's electrical activity. The processing of ECG signals is thus critical for recognising anomalies or the start of illnesses. Noises like powerline interference (PLI) and baseline wander (BW) tend to occur in ECG signals, if not eliminated properly they tend to degrade the signal quality considerably. Henceforth, removal of PLI and BW is a crucial task in the analysis of biomedical signals, especially ECG signals. In this paper, we used a newly developed method called circulant singular spectrum analysis (CiSSA) for the removal of BW and four stage cascaded Savitzky-Golay (SG) filter for the elimination of PLI from the ECG signals by retaining the morphological properties of ECG signal. The efficacy of the method proposed is endorsed by using MIT-BIH Arrhythmia database. The simulation results noticeably exhibit that the method performs better when compared with that of other existing state-of-art techniques using performance metrics like output signal-to-noise ratio (SNRout), root mean square error (RMSE), percent root mean square difference (PRD), and correlation coefficient (CC) at various levels of input signal-to-noise ratio power (SNRin). We have achieved an average SNRout of 24.48 and CC of 0.99. Hence, the intended approach can be used in preprocessing stage of ECG signal analysis.