Phonocardiographic sensing using deep learning for abnormal heartbeat detection

Latif, Siddique and Usman, Muhammad and Rana, Rajib and Qadir, Junaid (2018) Phonocardiographic sensing using deep learning for abnormal heartbeat detection. IEEE Sensors Journal, 18 (22). pp. 9393-9400. ISSN 1530-437X

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Abstract

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Submitted version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Date Deposited: 29 Jan 2019 00:18
Last Modified: 05 Feb 2019 01:33
Uncontrolled Keywords: abnormal heartbeat detection, phonocardiography signals, deep learning, recurrent neural networks
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1109/JSEN.2018.2870759
URI: http://eprints.usq.edu.au/id/eprint/34789

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