No abstract

Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N. However, no specific guideline exists on how to determine the appropriate parameter values for distinguishing congestive heart failure (CHF) from normal sinus rhythm (NSR) subjects in clinical application. In the present study, a thorough analysis on the selection of appropriate values of m, r and N for sample entropy (SampEn) and recently proposed fuzzy measure entropy (FuzzyMEn) is presented for distinguishing two group subjects. 44 long-term NRS and 29 long-term CHF RR interval recordings from http://www.physionet.org were used as the non-pathological and pathological data respectively. Extreme (>2 s) and abnormal heartbeat RR intervals were firstly removed from each RR recording and then the recording was segmented with a non-overlapping segment length N of 300 and 1000, respectively. SampEn and FuzzyMEn were performed Compared with SampEn, FuzzyMEn shows a better regularity when selecting the parameters m and r. In addition, FuzzyMEn shows a better relative consistency for distinguishing the two groups, that is, the results of FuzzyMEn in the NSR group were consistently lower than those in the CHF group while SampEn were not. The selections of m of 2 and 3 and r of 0.10 and 0.15 for SampEn and the selections of m of 1 and 2 whenever r (herein, rL = rG = r) are for FuzzyMEn (in addition to setting nL = 3 and nG = 2) were recommended to yield the fine classification results for the NSR and CHF groups.

A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy (F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in the test of normal ECG database versus arrhythmic ECG database, all ten QRS detection algorithms had good F1 results for these two databases (all >95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low F1 result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).

For evaluating performance of nonlinear features and iterative and non-iterative classification algorithms (i.e. kernel support vector machine (KSVM), random forest (RaF), least squares SVM (LS-SVM) and multi-surface proximal SVM based oblique RaF (ORaF) for ECG quality assessment we compared the four algorithms on 7 feature schemes yielded from 27 linear and nonlinear features including four features derived from a new encoding Lempel-Ziv complexity (ELZC) and the other 26 features. The seven feature schemes include the first scheme consisting of 7 waveform features, the second consisting of 15 waveform and frequency features, the third consisting of 19 waveform, frequency and approximate entropy (ApEn) features, the fourth consisting of 19 waveform, frequency and permutation entropy (PE) features, the fifth consisting of 19 waveform, frequency and ELZC features, the sixth consisting of 23 waveform, frequency, PE and ELZC features, and the last consisting of all 27 features. Up to 1500 mobile ECG recordings from the Physionet/Computing in Cardiology Challenge 2011 were employed in this study. Three indices i.e. sensitivity (Se), specificity (Sp) and accuracy (Acc), were used for evaluating performances of the four classifiers on the seven feature schemes, respectively. The experiment results indicated PE and ELZC can help to improve performance of the aforementioned four classifiers for assessing ECG quality. Using all features besides ApEn features obtained the best performances for each classifier. For this sixth scheme, the LS-SVM yielded the highest Acc of 92.20% on hidden test data, as well as a relatively high Acc of 93.60% on training data. Compared with the other classifiers, the LS-SVM classifier also demonstrated the superior generalization ability.

Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects.