Key result
Novel entropy threshold improves CHF and AF detection with ~8-point higher AUC over standard methods.
Why the study?
Initializing Sample Entropy parameters usually relies on expert experience, where improper assignments can lead to invalid values, inconsistency, and low statistical significance.
Does a physically meaningful tolerance threshold (r_p) improve the stability and accuracy of Sample Entropy in detecting cardiovascular diseases like CHF and AF compared to a traditional threshold (r_t)?
Does a physically meaningful tolerance threshold (r_p) improve the stability and accuracy of Sample Entropy in detecting cardiovascular diseases like CHF and AF compared to a traditional threshold (r_t)?
p-value: p=<0.05
Adjusting the Sample Entropy tolerance threshold based on ECG sampling resolution improves its mathematical stability and diagnostic performance for detecting heart failure and atrial fibrillation.
May enhance sample entropy stability for CHF/AF detection; hypothesis-generating pending prospective validation.
Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning ( r p ), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed r p against the traditional threshold ( r t ). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed r p had better stability than r t , hence more adaptive to detect cardiovascular diseases of CHF and AF.
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Xiong et al. (2019) studied Congestive Heart Failure and Atrial Fibrillation (n=156). Physically meaningful tolerance threshold (r_p) vs. Traditional tolerance threshold (r_t) was evaluated on Detection of cardiovascular diseases (AUC and statistical significance) (p=<0.05). The proposed physically meaningful threshold (r_p) avoided invalid entropy values and demonstrated higher AUC values (e.g., up to 77.63% vs 69.91%) and better stability than the traditional threshold (r_t) for detecting congestive heart failure and atrial fibrillation.
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