Sourav Kumar Mukhopadhyay1, Sridhar Krishnan2. 1. Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada. Electronic address: sourav.mukhopadhyay@ryerson.ca. 2. Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada. Electronic address: krishnan@ryerson.ca.
Abstract
BACKGROUND AND OBJECTIVE: An efficient and robust electrocardiogram (ECG) denoising technique caters three-fold benefits in the subsequent processing steps: first, it helps improving the accuracy of extracted features. Second, the improved accuracy in the extracted features enhances the performance as well as the reliability of computerised cardiovascular-disease diagnosis systems, and third, it also makes the interpretation task easier for the clinicians. Albeit a number of ECG denoising techniques are proposed in the literature, but most of these techniques suffer from one or more of the following drawbacks: i) model or function dependency, ii) sampling-rate dependency, or iii) high time-complexity. METHODS: This paper presents a singular spectrum analysis (SSA)-based ECG denoising technique addressing most of these afore-mentioned shortcomings. First, a trajectory matrix of dimension K × L is formed using the original one-dimensional ECG signal of length N. In SSA operation the parameter L, which is denoted as the window-length, plays a very important role and is related to the sampling frequency of the signal. In this research the value of L is calculated dynamically based on the morphological property of the ECG signal. Then, the matrix is decomposed using singular value decomposition technique, and the principal components (PC) of the original ECG signal are computed. Next, the reconstructed components (RC) are calculated from the PCs, and all the RCs are filtered through Butterworth bandpass and notch filters. An optimum number of filtered RCs are retained based on their significance. Finally, these retained RCs are summed up to obtain the denoised ECG signal. RESULTS: Evaluation result shows that the proposed technique outperforms state-of-the-art ECG denoising methods; in particular, the mean opinion score of the denoised signal falls under the category 'very good' as per the gold standard subjective measure. CONCLUSIONS: Both the quantitative and qualitative distortion measure metrics show that the proposed ECG denoising technique is robust enough to filter various noises present in the signal without jeopardizing the clinical content. The proposed technique could be adapted for denoising other biomedical signals exhibiting periodic or quasi-periodic nature such as photoplethysmogram and esophageal pressure signal.
BACKGROUND AND OBJECTIVE: An efficient and robust electrocardiogram (ECG) denoising technique caters three-fold benefits in the subsequent processing steps: first, it helps improving the accuracy of extracted features. Second, the improved accuracy in the extracted features enhances the performance as well as the reliability of computerised cardiovascular-disease diagnosis systems, and third, it also makes the interpretation task easier for the clinicians. Albeit a number of ECG denoising techniques are proposed in the literature, but most of these techniques suffer from one or more of the following drawbacks: i) model or function dependency, ii) sampling-rate dependency, or iii) high time-complexity. METHODS: This paper presents a singular spectrum analysis (SSA)-based ECG denoising technique addressing most of these afore-mentioned shortcomings. First, a trajectory matrix of dimension K × L is formed using the original one-dimensional ECG signal of length N. In SSA operation the parameter L, which is denoted as the window-length, plays a very important role and is related to the sampling frequency of the signal. In this research the value of L is calculated dynamically based on the morphological property of the ECG signal. Then, the matrix is decomposed using singular value decomposition technique, and the principal components (PC) of the original ECG signal are computed. Next, the reconstructed components (RC) are calculated from the PCs, and all the RCs are filtered through Butterworth bandpass and notch filters. An optimum number of filtered RCs are retained based on their significance. Finally, these retained RCs are summed up to obtain the denoised ECG signal. RESULTS: Evaluation result shows that the proposed technique outperforms state-of-the-art ECG denoising methods; in particular, the mean opinion score of the denoised signal falls under the category 'very good' as per the gold standard subjective measure. CONCLUSIONS: Both the quantitative and qualitative distortion measure metrics show that the proposed ECG denoising technique is robust enough to filter various noises present in the signal without jeopardizing the clinical content. The proposed technique could be adapted for denoising other biomedical signals exhibiting periodic or quasi-periodic nature such as photoplethysmogram and esophageal pressure signal.