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Entropy profiling to detect ST change in heart rate variability signals
conference contribution
posted on 2019-01-01, 00:00 authored by Radhagayathri Krishnavilas Udhayakumar, Chandan KarmakarChandan Karmakar, Marimuthu PalaniswamiElevation 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.
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Event
IEEE Engineering in Medicine & Biology Society. Conference (2019 : 41st : Berlin, Germany)Pagination
4588 - 4591Publisher
IEEELocation
Berlin, GermanyPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2019-07-23End date
2019-07-27ISBN-13
9781538613115Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
EMBC 2019 : Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyUsage metrics
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