Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks

Cardiac Auscultation, an integral part of the physical examination of a patient, is essential for early diagnosis of cardiovascular diseases (CVDs). The ability to accurately diagnose the heart sounds requires experience and expertise, which is lacking in doctors in the early years of clinical pract...

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Bibliographic Details
Published in2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2021; pp. 713 - 717
Main Authors Megalmani, Drishti Ramesh, G, Shailesh B, Rao M V, Achuth, Jeevannavar, Satish S, Ghosh, Prasanta Kumar
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.11.2021
Subjects
Heart
Heart Sounds
Humans
Knowledge engineering
Medical services
Neural networks
Neural Networks, Computer
Pipelines
Sensitivity
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Summary:Cardiac Auscultation, an integral part of the physical examination of a patient, is essential for early diagnosis of cardiovascular diseases (CVDs). The ability to accurately diagnose the heart sounds requires experience and expertise, which is lacking in doctors in the early years of clinical practice. Thus, there is a need for an automatic diagnostic tool that would aid medical practitioners with their diagnosis. We propose novel hybrid architectures for classification of unsegmented heart sounds to normal and abnormal classes. We propose two methods, with and without the conventional feature extraction step in the classification pipeline. We demonstrate that the F score using the approach with conventional feature extraction is 1.25 (absolute) more than using a baseline implementation on the Physionet dataset. We also introduce a mechanism to tag predictions as unsure and compare results with a varying threshold.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9629596