Nonlinear Quality Indices Based on a Novel Lempel-Ziv Complexity for Assessing Quality of Multi-Lead ECGs Collected in Real Time
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초록
We compared a novel encoding LempelZiv complexity (ELZC) with three common complexity algorithmsi.e., approximate entropy (ApEn), sample entropy (SampEn), and classic LempelZiv complexity (CLZC) soas to determine a satisfied complexity and its corresponding quality indices for assessing quality of multileadelectrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series inorder to compare their performance in terms of discerning randomness and the inherent irregularity within timeseries. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, weinvestigated their change trend in five artificial synthetic noisy ECGs containing different noises at severalsignal noise ratios. Finally, three quality indices based on the ELZC of the multilead ECG were proposed toassess the quality of 862 real 12lead ECGs from the MIT databases. The results showed the ELZC coulddiscern randomness and the inherent irregularity within six artificial time series, and also reflect content levelof different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indicesof the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were moresuitable for multilead ECG quality assessment than the other three algorithms.
Abstract
We compared a novel encoding LempelZiv complexity (ELZC) with three common complexity algorithmsi.e., approximate entropy (ApEn), sample entropy (SampEn), and classic LempelZiv complexity (CLZC) soas to determine a satisfied complexity and its corresponding quality indices for assessing quality of multileadelectrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series inorder to compare their performance in terms of discerning randomness and the inherent irregularity within timeseries. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, weinvestigated their change trend in five artificial synthetic noisy ECGs containing different noises at severalsignal noise ratios. Finally, three quality indices based on the ELZC of the multilead ECG were proposed toassess the quality of 862 real 12lead ECGs from the MIT databases. The results showed the ELZC coulddiscern randomness and the inherent irregularity within six artificial time series, and also reflect content levelof different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indicesof the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were moresuitable for multilead ECG quality assessment than the other three algorithms.
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