Real-time filtering adaptive algorithms for non-stationary noise in electrocardiograms. (February 2022)

• Record Type: Journal Article
• Authors: Tulyakova, Nataliya; Trofymchuk, Oleksandr
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: ECG signal processing -- Non-stationary noise -- EMG noise -- Real time adaptive filtering; 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.2021.103308
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A non-stationary noise with a priori unknown characteristics often contaminates ECG signals. The proposed algorithms suppress non-stationary noise well and preserve ECG waveform and amplitude-time parameters. Signal is processed in real time with little delay of obtaining the filter output. The method does not process QRS-complex at a very low noise but suppresses noise with different variance well. Re-filtering for not low noise levels is beneficial. Abstract: A non-stationary noise with a previously unknown intensity level often contaminates electrocardiogram (ECG) signals. Therefore, provision of high quality suppression of the non-stationary noise in ECG is a vital task to be performed. A new lightweight adaptive method has been proposed for real-time filtering of non-stationary (from the point of view of its variance) noise in ECG with noise- and signal-dependent switching filters, appropriate for processing a local vicinity of the current input signal sample. This method does not require time for filter parameters adaptation and a priori information about the noise variance. A one- and a two-pass algorithm on the simple optimal Savitzky & Golay filters and on the linear averaging filter have been developed on the basis of the proposed method. There is also an algorithm suggested applying a re-filtering only when the identifiers used in the method define a not low noise level. The integral and local statistical estimates of filters' efficiency have been obtained from numerical simulations over mean-square error (MSE), maximum absolute error (MAE), and signal-to-noise ratio (SNR) for a model ECG signal under different levels of Gaussian noise. Filtering efficiency was estimated with the real signals taken from physionet.org database. The filter parameters were chosen by numerical simulations for a typical P-QRS-T cycle with corresponding signal sampling rate and scale considered. For a wide range from low to high noise levels (input SNR belongs to the interval from 25 to 0 dB), the statistical estimates of efficiency have been obtained as follows: for an ECG sampled at 360 Hz (taken from NSTDB), inside QRS-complex, the SNR increases by 2.5–6.7 dB, the MSE decreases in 1.7–4.3 times and the MAE decreases in 1.3–2.2 times; inside the segments prior to and following QRS-complex, the SNR, on an average, increase by 8.6–13.2 dB and the MSE decreases in 7.1–19.2 times, and the MAE decreases in 2.4–5.1 times. For an ECG sampled at 1 kHz (taken from PTB), inside QRS-complex, the SNR increases by 5.2–8 dB, the MSE and the MAE decrease in 3.2–6 times and 1.9–2.7 times, respectively; outside QRS, the SNR increases by 10.2–15.8 dB, the MSE and the MAE decrease in 10.5–36 times and in 2.6–5.2 times, respectively. For an ECG sampled at 250 Hz (from CUDB), the local indicators of efficiency are: inside QRS-complex, the SNR increases by 2.9–6.6 dB, the MSE and the MAE decrease in 1.8–4 times and in 1.4–2.3 times, respectively; outside QRS-complex, the SNR, on an average, increase by 4.7–11.6 dB, the MSE and the MAE decrease in 3.3–10.9 times and in1.7–3.9 times. Additionally, the filters' efficiency has been estimated as to suppression of real electromyographic (EMG) noise with significantly different variance and the proposed algorithms have been compared with other filters. A noise-free ECGs during 5 min sampled at 360 Hz were contaminated with highly non-stationary EMG noise from a muscle artifact (MA) record of different intensity (input SNR varies from 20 to −5 dB), the SNR improvement at the proposed algorithm output is 10–14 dB. The calculated quantitative estimates of efficiency confirm the high quality of non-stationary EMG noise suppression obtained with the adaptive algorithms suggested. Minute signal distortions and a high degree of noise suppression have been demonstrated. Good performance and high filter quality for various real signals with non-stationary EMG noise have been shown. ECG amplitude-time parameters and waveforms, including pathological changes, are shown to be well-preserved.