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A fast noise-tolerant ECG feature recognition algorithm based on probabilistic analysis of gradient discontinuity
1Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
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 5.42ms, 60% lower than the cardiologist's 13.57ms. 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.