A fast noise-tolerant ECG feature recognition algorithm based on probabilistic analysis of gradient discontinuity

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Journal Article
Journal of Electrocardiology, 2017, 50 (4), pp. 491 - 503
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© 2017 Elsevier Inc. Purpose Improvement in real-time electrocardiogram (ECG) interpretation is still needed, especially for QT estimation. This paper proposes a fast algorithm for ECG feature recognition, based on locating turning points in the waveform gradient. Methods The algorithm places the fiducial point at the maximal value of a probabilistic decision function, assessing line intervals of best fit before and after the point and the point location relative to R-wave peaks already found. Results Fiducial points were successfully located for the 30 heartbeats annotated by a cardiologist of all 10 normal sinus rhythm records from the PhysioNet QT Database. For a given subject, the algorithm's QT estimation had superior repeatability, with intrasubject QT standard deviation just 5.42 ms, 60% lower than the cardiologist's 13.57 ms. Initial tests suggest immunity to noise of standard deviation up to about 9% of the signal, depending on noise type. Conclusions The proposed algorithm is fast to calculate and noise-tolerant, and has shown improved repeatability in its QT estimation compared to a cardiologist.
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