Cutting Weights of Deep Learning Models for Heart Sound Classification: Introducing a Knowledge Distillation Approach

Zikai Song, Lixian Zhu, Yiyan Wang, Mengkai Sun, Kun Qian, Bin Hu, Yoshiharu Yamamoto, Bjorn W. Schuller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. In recent years, intelligent auxiliary diagnosis of CVDs based on computer audition has become a popular research field, and intelligent diagnosis technology is increasingly mature. Neural networks used to monitor CVDs are becoming more complex, requiring more computing power and memory, and are difficult to deploy in wearable devices. This paper proposes a lightweight model for classifying heart sounds based on knowledge distillation, which can be deployed in wearable devices to monitor the heart sounds of wearers. The network model is designed based on Convolutional Neural Networks (CNNs). Model performance is evaluated by extracting Mel Frequency Cepstral Coefficients (MFCCs) features from the PhysioNet/CinC Challenge 2016 dataset. The experimental results show that knowledge distillation can improve a lightweight network's accuracy, and our model performs well on the test set. Especially, when the knowledge distillation temperature is 7 and the weight α is 0.1, the accuracy is 88.5 %, the recall is 83.8 %, and the specificity is 93.6 %.Clinical relevance-A lightweight model of heart sound classification based on knowledge distillation can be deployed on various hardware devices for timely monitoring and feedback of the physical condition of patients with CVDs for timely provision of medical advice. When the model is deployed on the medical instruments of the hospital, the condition of severe and hospitalised patients can be timely fed back and clinical treatment advice can be provided to the clinicians.

Original languageEnglish
Title of host publication2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324471
DOIs
StatePublished - 2023
Externally publishedYes
Event45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia
Duration: 24 Jul 202327 Jul 2023

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/TerritoryAustralia
CitySydney
Period24/07/2327/07/23

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