Analysis of Statistical Methods for Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

In this study, we analyzed three statistical methods for automatic detection of Atrial Fibrillation (AF) and Congestive Heart Failure (CHF) based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized Root Mean Square of Successive RR Differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using MIT-BIH AF (n=23), MIT-BIH NSR (n=18), BIDMC CHF (n=13) and the CHF RRI (n=25) databases. Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity.

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