A denoising performance comparison based on ECG Signal Decomposition and local means filtering

Abstract The electrocardiogram (ECG) signal is popular and extensively used as a diagnostic tool for revealing several diseases related to the heart. Unfortunately, its sensitive to noises from various sources. Denoising these noises remains a hard and major challenge task, although there are some existing methods for denoising, they remain limited to a few types of noises with significant distortion after the filtering process. In this paper, we investigated a hybrid system that can deal with different types of noises, with different levels of SNR, conserving all the signal features with the minimum distortion. The hybrid system consists of a decomposition method followed by local means (LM) filtering. The best candidates used for the signal decomposition are the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). To demonstrate the effectiveness of the proposed method, several real ECG recordings and synthetic ECG infected by a different type of noise (i.e., baseline wander, muscle artifacts, electrode motion, mix noise, white noise, colored noise) with different levels of signal to noise ratios (24, 18, 6, 0, −6) dB are used in our numerical analysis. The performance evaluation of our proposed system is made using the following metrics: SNR improvement, signal-to-noise and distortion ratio (SINAD), the approximate entropy, and the fuzzy entropy. The experimental results emphasized that the Ensemble Empirical Mode Decomposition (EEMD) followed by local means (LM) outperforms all the comparison methods, in terms of all the metrics used, with few exceptions where the Empirical Mode Decomposition (EMD) followed by local means (LM) and Discrete Wavelet Transform (DWT) followed by local means (LM) performed well.