Key result
A novel Ensemble Kalman Filter method for denoising ECG signals achieved an average signal to noise ratio of 10.96, a Percentage Root Difference of 150.45, and a correlation coefficient of 0.959.
Why the study?
Wearable electronic devices acquire physiological signals in home settings that are usually contaminated with high-level noise, creating a need for an efficient ECG filtering scheme.
Does the Ensemble Kalman Filter improve ECG signal denoising compared to other filtering algorithms?
Does the Ensemble Kalman Filter improve ECG signal denoising compared to other filtering algorithms?
The Ensemble Kalman Filter provides an efficient method for denoising ECG signals, which is particularly relevant for improving signal quality from wearable electronics.
May improve wearable ECG signal quality; leaves open superiority over existing filters in clinical settings.
Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.
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Sarafan et al. (2022) studied ECG signal noise. Ensemble Kalman Filter (EnKF) vs. SG filter, EEMD, NLMS, RLS filter, TVD, Wavelet, and EKF was evaluated on Average signal to noise ratio (SNR), Percentage Root Difference, and correlation coefficient. A novel Ensemble Kalman Filter method for denoising ECG signals achieved an average signal to noise ratio of 10.96, a Percentage Root Difference of 150.45, and a correlation coefficient of 0.959.
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