Phonocardiographic sensing using deep learning for abnormal heartbeat detection

Article


Latif, Siddique, Usman, Muhammad, Rana, Rajib and Qadir, Junaid. 2018. "Phonocardiographic sensing using deep learning for abnormal heartbeat detection." IEEE Sensors Journal. 18 (22), pp. 9393-9400. https://doi.org/10.1109/JSEN.2018.2870759
Article Title

Phonocardiographic sensing using deep learning for abnormal heartbeat detection

ERA Journal ID4437
Article CategoryArticle
AuthorsLatif, Siddique (Author), Usman, Muhammad, Rana, Rajib (Author) and Qadir, Junaid (Author)
Journal TitleIEEE Sensors Journal
Journal Citation18 (22), pp. 9393-9400
Number of Pages8
Year2018
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1530-437X
1558-1748
Digital Object Identifier (DOI)https://doi.org/10.1109/JSEN.2018.2870759
Web Address (URL)https://ieeexplore.ieee.org/document/8466894
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.

Keywordsabnormal heartbeat detection, phonocardiographysignals, deep learning, recurrent neural networks
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsInformation Technology University, Pakistan
COMSATS University Islamabad, Pakistan
Institute for Resilient Regions
Institution of OriginUniversity of Southern Queensland
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