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Open AccessArticle

Improving Accuracy of Heart Failure Detection Using Data Refinement

1
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
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The Hamlyn Centre/Department Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
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Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 520; https://doi.org/10.3390/e22050520
Received: 23 March 2020 / Revised: 25 April 2020 / Accepted: 30 April 2020 / Published: 2 May 2020
(This article belongs to the Special Issue Entropy in Biomedical Engineering)
Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection. View Full-Text
Keywords: cardiovascular time series; congestive heart failure; data refinement; heart rate variability; sample entropy cardiovascular time series; congestive heart failure; data refinement; heart rate variability; sample entropy
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Xiong, J.; Liang, X.; Zhao, L.; Lo, B.; Li, J.; Liu, C. Improving Accuracy of Heart Failure Detection Using Data Refinement. Entropy 2020, 22, 520.

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