The study “Learning Representations from Heart Sound: A Comparative Study on Shallow and Deep Models” explores machine learning and deep learning models for Heart Sound Status (HSS) monitoring. Conducted by various institutions including the Ministry of Education (Beijing Institute of Technology), The University of Tokyo, and Imperial College London, the research focuses on Phonocardiogram (PCG)/Heart Sound Classification from 2013 to 2024. Different models such as CTENN, Bispectrum + ViT, and MWRS-BFSC + CNN2D are discussed for heart sound classification. The study aims to improve the accuracy and efficiency of diagnosing heart conditions through the analysis of heart sounds. Sik-Ho Tsang, the author of the study, provides additional insights and resources related to heart sound classification in his other paper readings. The research contributes to the advancement of technology in healthcare by utilizing machine learning and deep learning models to analyze heart sounds for monitoring and diagnosing heart conditions.
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Source link: https://sh-tsang.medium.com/brief-review-learning-representations-from-heart-sound-a-comparative-study-on-shallow-and-deep-06fa92ff20e8?source=rss——artificial_intelligence-5
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Comparing shallow and deep models for heart sound representations. #Cardiology
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