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Sensors 2015, 15(7), 17693-17714;

Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves

Electrical and Computer Engineering in Medicine Group, University of British Columbia and BC Children's Hospital, Vancouver, BC V6H 3N1, Canada
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuernbeg, Haberstr. 2, 91058 Erlangen, Germany
School of Electrical and Electronic Engineering, University of Adelaide, Adelaide SA 5005, Australia
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 1 June 2015 / Revised: 8 July 2015 / Accepted: 10 July 2015 / Published: 21 July 2015
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1460 KB, uploaded 21 July 2015]


Background: There are limited studies on the automatic detection of T waves in arrhythmic electrocardiogram (ECG) signals. This is perhaps because there is no available arrhythmia dataset with annotated T waves. There is a growing need to develop numerically-efficient algorithms that can accommodate the new trend of battery-driven ECG devices. Moreover, there is also a need to analyze long-term recorded signals in a reliable and time-efficient manner, therefore improving the diagnostic ability of mobile devices and point-of-care technologies. Methods: Here, the T wave annotation of the well-known MIT-BIH arrhythmia database is discussed and provided. Moreover, a simple fast method for detecting T waves is introduced. A typical T wave detection method has been reduced to a basic approach consisting of two moving averages and dynamic thresholds. The dynamic thresholds were calibrated using four clinically known types of sinus node response to atrial premature depolarization (compensation, reset, interpolation, and reentry). Results: The determination of T wave peaks is performed and the proposed algorithm is evaluated on two well-known databases, the QT and MIT-BIH Arrhythmia databases. The detector obtained a sensitivity of 97.14% and a positive predictivity of 99.29% over the first lead of the validation databases (total of 221,186 beats). Conclusions: We present a simple yet very reliable T wave detection algorithm that can be potentially implemented on mobile battery-driven devices. In contrast to complex methods, it can be easily implemented in a digital filter design. View Full-Text
Keywords: arrhythmia; affordable healthcare; moving averages arrhythmia; affordable healthcare; moving averages
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Elgendi, M.; Eskofier, B.; Abbott, D. Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves. Sensors 2015, 15, 17693-17714.

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