Subject-specific detection of ventricular tachycardia using convolutional neural networks

Chandra, B S and Challa, Subrahmanya Sastry and Jana, Soumya (2017) Subject-specific detection of ventricular tachycardia using convolutional neural networks. In: 43rd Computing in Cardiology Conference, CinC, 11-14 September, 2016, Vancouver; Canada.

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Onset of ventricular tachycardia (VT) is clinically significant, including as a trigger to defibrillator implants. In this paper, we propose a reliable technique to detect such onset using convolutional neural networks (CNNs). The proposed CNN adds convolution and pooling layers below the input layer and above the hidden and output layers of usual neural network (NN). Such layers would learn suitable linear features from training data, while eliminating the need to extract the traditionally used adhoc features. Employing such subject-specific features, we reported the performance of the proposed classifier using Creighton University ventricular tachyarrhythmia database (CUVT). In particular, we achieved mean (± standard deviation) performance of 95.6 (± 00.6) using subject-specific evaluation scheme over 100 random independent iterations.

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IITH Creators:
IITH CreatorsORCiD
Challa, Subrahmanya SastryUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Cardiology; Classification (of information); Neural networks Convolutional neural network; Evaluation scheme; Linear feature; Neural network (nn); Standard deviation; Subject-specific; Ventricular tachyarrhythmias; Ventricular tachycardia
Subjects: Others > Electricity
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Divisions: Department of Electrical Engineering
Department of Mathematics
Depositing User: Team Library
Date Deposited: 10 Apr 2017 07:07
Last Modified: 01 Sep 2017 10:15
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