Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

摘要

The selection of an appropriate wavelet is an essential issue that should be addressed in the wavelet-based filtering of electrocardiogram (ECG) signals. Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion E_com based on multiple criteria related to entropy and energy is proposed in this paper to search for an optimal base wavelet for a specific ECG signal. Taking account of the decomposition capability of wavelets and the similarity in information between the decomposed coefficients and the analyzed signal, the proposed E_com criterion integrates eight criteria, i.e., energy, entropy, energy-to-entropy ratio, joint entropy, conditional entropy, mutual information, relative entropy, as well as comparison information entropy for optimal wavelet selection. The experimental validation is conducted on the basis of ECG signals of sixteen subjects selected from the MIT-BIH Arrhythmia Database. The E_com is compared with each of these eight criteria through four filtering performance indexes, i.e., output signal to noise ratio (SNR_o), root mean square error (RMSE), percent root mean-square difference (PRD) and correlation coefficients. The filtering results of ninety-six ECG signals contaminated by noise have verified that E_com has outperformed the other eight criteria in the selection of best base wavelets for ECG signal filtering. The wavelet identified by the E_com has achieved the best filtering performance than the other comparative criteria. A hypothesis test also validates that SNR_o, RMSE, PRD and correlation coefficients of E_com are significantly different from those of the shape-matched approach (α = 0.05, two-sided t- test).