Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer



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BibTex format

author = {Xiong, J and Liang, X and Zhao, L and Lo, B and Li, J and Liu, C},
doi = {10.3390/e22050520},
journal = {Entropy: international and interdisciplinary journal of entropy and information studies},
pages = {520--520},
title = {Improving accuracy of heart failure detection using data refinement},
url = {},
volume = {22},
year = {2020}

RIS format (EndNote, RefMan)

AB - 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.
AU - Xiong,J
AU - Liang,X
AU - Zhao,L
AU - Lo,B
AU - Li,J
AU - Liu,C
DO - 10.3390/e22050520
EP - 520
PY - 2020///
SN - 1099-4300
SP - 520
TI - Improving accuracy of heart failure detection using data refinement
T2 - Entropy: international and interdisciplinary journal of entropy and information studies
UR -
UR -
UR -
VL - 22
ER -