Qualitative and Quantitative Performance Comparison of ECG Noise Reduction and Signal Enhancement Method based on Various Digital Filter Designs and Discrete Wavelet Transform

Abstract

Electrocardiogram (ECG) is a graphical representation and bio-signal recording of cardiac electrical activity. It conveys a great amount of information regarding structural and functional performance of the heart. Hence, ECG plays an essential role in the cardiac assessment, abnormality detection and clinical diagnosis. A clean ECG signal plays an imperative and vital role in the primary clinical analysis and diagnosis of cardiac diseases. Unfortunately, the greatest obstacle in analyzing and interpreting an ECG signal is the presence of unwanted artifacts and noises as they contaminate and degrade the quality of the ECG signals. As a result, removal of unwanted artifacts and noises from an ECG signal becomes an indispensable task to ensure an accurate and reliable ECG analysis could be performed. In this study, many ECG noise reduction and enhancement methods based on various digital filter designs, as well as discrete wavelet transform with various mother wavelets, are modelled to investigate and benchmark their performance in term of Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE). This testing are based on ten randomly selected ECG datasets acquired from ECG-ID Database (ecgiddb) which available in PhysioNet. Based on structured qualitative and quantitative performance analysis, results conclude that the discrete wavelet transform with db8 as mother wavelet outperforms the Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filter designs in de-noising and enhancing a raw ECG signal with highest SNR value of 4.4148, at the same time achieve significant lowest RMSE value of 4.0767. This is due to the reason that discrete wavelet transform method has advantages in analyzing the ECG signal in both time and frequency domain, thus causing less distortion to ECG signal.