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Sensors 2016, 16(10), 1580; doi:10.3390/s16101580

Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Academic Editor: Hsiung-Cheng Lin
Received: 27 June 2016 / Revised: 31 August 2016 / Accepted: 19 September 2016 / Published: 27 September 2016
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Abstract

Abnormal intra-QRS potentials (AIQPs) are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia. View Full-Text
Keywords: abnormal intra-QRS potentials; ventricular late potentials; radial basis function neural network; orthogonal least squares; ventricular tachycardia abnormal intra-QRS potentials; ventricular late potentials; radial basis function neural network; orthogonal least squares; ventricular tachycardia
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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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Lin, C.-C. Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network. Sensors 2016, 16, 1580.

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