A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter

TL;DR

This paper introduces a new ECG denoising method using the Ensemble Kalman Filter, demonstrating superior noise reduction performance compared to various existing filtering algorithms on noisy ECG data.

Contribution

The paper develops a novel ECG denoising scheme based on Ensemble Kalman Filter and compares it with multiple existing filtering techniques, showing improved effectiveness.

Findings

Average SNR of 10.96 achieved with the proposed method

Correlation coefficient of 0.959 indicating high signal fidelity

Outperforms other filtering algorithms in noise reduction

Abstract

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.