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SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine Intelligence Read chapter Read first chapter

2024 | OriginalPaper | Chapter

HeartBeatNet: Unleashing the Power of Attention in Cardiology

Authors : Gurjot Singh, Anant Mehta, Vinay Arora

Published in: Computational Intelligence and Network Systems

Publisher: Springer Nature Switzerland

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Abstract

Cardiovascular disease diagnosis and prompt medical care depend critically on the classification of heart sounds. In recent years, deep learning-based approaches have shown promising results in automating the process of heart sound categorization. This paper proposes a model HeartBeatNet (an attention UNet-based system) for heart sound classification that demonstrates comparatively better performance. The proposed system combines the strengths of attention mechanisms and the UNet architecture to effectively capture relevant features and to make accurate predictions. The system is trained on the PhysioNet/Cinc 2016 dataset consisting of annotated heart sound recordings, which are first converted into Mel Spectrograms before feeding into the UNet based network. The results indicate that the proposed system achieves high accuracy of 95.14%, sensitivity of 90.00%, and specificity of around 96.72% to classify various heart sound abnormalities.

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Metadata
Title
HeartBeatNet: Unleashing the Power of Attention in Cardiology
Authors
Gurjot Singh
Anant Mehta
Vinay Arora
Copyright Year
2024
Book
Computational Intelligence and Network Systems

Print ISBN: 978-3-031-48983-9

Electronic ISBN: 978-3-031-48984-6

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-48984-6_2

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