Entropy Profiling to Detect ST Change in Heart Rate Variability Signals

Elevation or depression in an electrocardiographic ST segment is an important indication of cardiac Ischemia. Computer-aided algorithms have been proposed in the recent past for the detection of ST change in ECG signals. Such algorithms are accompanied by difficulty in locating a functional ST segment from the ECG. Laborious signal processing tasks have to be carried out in order to precisely locate the start and end of an ST segment. In this work, we propose to detect ST change from heart rate variability (HRV) or RR-interval signals, rather than the ECG itself. Since HRV analysis does not require ST segment localization, we hypothesize an easier and more accurate automated ST change detection here. We use the recent concept of entropy profiling to detect ST change from RR interval data, where the estimation corresponds to irregularity information contained in the respective signals. We have compared results of SampEn, FuzzyEn and TotalSampEn (entropy profiling) on 18 normal and 28 ST-changed RR interval signals. SampEn and FuzzyEn give maximum AUCs of 0.64 and 0.62 respectively, at the data length N = 750. T otalSampEn shows a maximum AUC of 0.92 at N = 50, clearly proving its effectiveness on short-term signals and an AUC of 0.88 at N = 750, proving its efficiency over SampEn and F uzzyEn.