Evaluation measures for adaptive PLI filters in ECG signal processing

Abstract

Many studies have been devoted to the adaptive power-line interference (PLI) filter design for ECG signal processing. However, almost all existing PLI suppression filters are developed for applications in which the presence of PLI noise is assumed a priori. Indiscriminate application of PLI suppression over an ECG signal that is free of PLI noise may deform ECG morphology, and even cause degraded performance of subsequent processing. To date, little work has been done on the possibility of ECG signal degradation by such filtering operation and the impact on further processing. In order to evaluate the difference between the original and the filtered pattern, this study proposes quantitative evaluation measures. The assessments include convergence time, the frequency tracking efficiency, the execution time and the relative statistics in time and frequency domain. Extensive experiments have been done with artificially and practically corrupted ECG signals for four existing PLI adaptive filtering techniques (Ahlstrom and Tompkinspsila, Pei and Tsengpsilas, Sopsilas and Ziarani and Konradpsilas algorithm). The results reveal that ECG signal distortion resulted from these existing adaptive filters. None of the existing algorithms outperform the others in all assessments. The proposed evaluation measures can also be used for the performance evaluation of the other types of artifact suppression, such as the baseline wander and EMG corruption contaminated in ECG, after minor modification. The proposed measures also make the optimal filter design under different constraints possible for ECG signal processing.