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Assessing the complexity of short-term heartbeat interval series by distribution entropy

Lookup NU author(s): Dr Chengyu Liu, Dr Ding Chang Zheng


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Complexity of heartbeat interval series is typically measured by entropy. Recent studies have found that sample entropy (SampEn) or fuzzy entropy (FuzzyEn) quantifies essentially the randomness, which may not be uniformly identical to complexity. Additionally, these entropy measures are heavily dependent on the predetermined parameters and confined to data length. Aiming at improving the robustness of complexity assessment for short-term RR interval series, this study developed a novel measure-distribution entropy (DistEn). The DistEn took full advantage of the inherent information underlying the vector-to-vector distances in the state space by probability density estimation. Performances of DistEn were examined by theoretical data and experimental short-term RR interval series. Results showed that DistEn correctly ranked the complexity of simulated chaotic series and Gaussian noise series. The DistEn had relatively lower sensitivity to the predetermined parameters and showed stability even for quantifying the complexity of extremely short series. Analysis further showed that the DistEn indicated the loss of complexity in both healthy aging and heart failure patients (both p < 0.01), whereas neither the SampEn nor the FuzzyEn achieved comparable results (all p a parts per thousand yen 0.05). This study suggested that the DistEn would be a promising measure for prompt clinical examination of cardiovascular function.

Publication metadata

Author(s): Li P, Liu CY, Li K, Zheng DC, Liu CC, Hou YL

Publication type: Article

Publication status: Published

Journal: Medical & Biological Engineering & Computing

Year: 2015

Volume: 53

Issue: 1

Pages: 77-87

Print publication date: 29/10/2014

Online publication date: 29/10/2014

Acceptance date: 20/10/2014

ISSN (print): 0140-0118

ISSN (electronic): 1741-0444

Publisher: Springer


DOI: 10.1007/s11517-014-1216-0


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Funder referenceFunder name
61201049National Natural Science Foundation of China
61471223National Natural Science Foundation of China
2014M561933China Postdoctoral Science Foundation
31200744National Natural Science Foundation of China