文章基本信息

标题：Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification
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作者：Tao Li ; Yibo Yin ; Kainan Ma 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：2
页码：54
DOI：10.3390/info12020054
出版社：MDPI Publishing
摘要：Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters. We segmented the original heart sound signal and performed a short-time Fourier transform (STFT) to obtain the frequency domain features. These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification. Considering the imbalance of positive and negative samples, we introduced FocalLoss as the loss function, verified our network model with multiple random verifications, and, hence, obtained a better classification result. Our main purpose is to design a lightweight network structure that is easy for hardware implementation. Compared with the results of the latest literature, our model only uses 4.29 K parameters, which is 1/10 of the size of the state-of-the-art work.
关键词：heart sound; convolutional network; FocalLoss; lightweight heart sound ; convolutional network ; FocalLoss ; lightweight