| Summary: | It is well known that electrocardiogram heartbeats are substantial for cardiac disease diagnosis. In this paper, the best time scale was investigated to recognize congestive heart failure (CHF) based on heart rate variability (HRV) measures. The classifications were performed on seven different time scales with a support vector machine classifier. Nine HRV measures, including three time-domain measures, three frequency-domain measures, and three nonlinear-domain measures, were taken as feature vectors for classifier on each time scale. A total of 83 subjects with RR intervals were analyzed, of which 54 cases were normal and 29 patients were suffering from CHF in PhysioNet databases. The classifying results using tenfold cross-validation method achieved the best performance of a sensitivity, specificity, and accuracy of 86.7%, 98.3%, and 94.4%, respectively, on the 2-h time scale. Moreover, by introducing only three nonstandard HRV features extracted from the trends of HRV measures on time scales, it achieved a better performance of a sensitivity of 93.3%, specificity of 98.3%, and an accuracy of 96.7%. The impressive performance of discrimination power on the 2-h time scale and the trends of HRV measures on time scales indicate that multiple time scales play significant roles in detecting CHF and can be valuable in expressing useful knowledge in medicine.
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