Distribution entropy for short-term QT interval variability analysis: a comparison between the heart failure and normal control groups

Previous studies have proved the beat to beat QT interval variability (QTV) helpful for evaluating the cardiac function. A common method for quantifying QTV is the QT variability index (QTVI). Owing to the nonlinear nature of QTV data, advanced entropy methods, such as the sample entropy (SampEn), were also used. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with conventional methods for especially short length data. We thus aimed, in this study, to investigate the short-term QTV in heart failure (HF) patients by DistEn. Thirty-four HF patients and 33 healthy subjects were studied, and their QTV series were analyzed by DistEn, and traditional SampEn, fuzzy entropy (FuzzyEn), and QTVI. Results demonstrated a significantly increased DistEn of QTV in HF group (p < 0.001). No significant difference was found in both SampEn and FuzzyEn between the two groups. In addition, results suggested a significantly increased QTVI in HF group (p < 0.01). Pearson correlation analysis showed that DistEn was significantly related to QTVI in HF group (p < 0.05), whereas there was no significant relation between them in healthy group. Results indicated that DistEn analysis of QTV may provide a valuable additional feature for evaluating the cardiac functioning in HF patients.