ViTDFNN: A Vision Transformer Enabled Deep Fuzzy Neural Network for Detecting Sleep Apnea-Hypopnea Syndrome in the Internet of Medical Things
Authors: 
Na Ying, Hongyu Li, Zhi Zhang, Yong Zhou, Huahua Chen, Meng YangAuthors Info & Claims
Pages 82 - 93
Abstract
Sleep apnea-hypopnea syndrome (SAHS) seriously affects human sleep, so it is necessary to find the disease as early as possible. However, most of traditional models use convolutional neural networks, which are ineffective in capturing image details. In addition, due to the fuzzy and uncertain information in the SaO2 signal image, the detection accuracy of traditional models is low. Therefore, we propose a vision transformer enabled deep fuzzy neural network (ViTDFNN) to detect SAHS in an Internet of Medical Things platform. The ViTDFNN model can analyze the patients vital signs and sleep status on time and identify potential SAHS conditions. It first uses a vision transformer to learn the features of the image so that global information and long-range dependencies can be learned. Subsequently, it uses VGG to extract the features of the image to learn the local information, which is convenient for distinguishing the texture, edge, and other details. Finally, it linearly fuses the two image features and inputs them into a deep fuzzy neural network (DFNN) and fully connected network to detect the patients symptoms. The DFNN can convert each deterministic pixel into an uncertain pixel, thereby solving the fuzzy and uncertain information. In addition, the ViTDFNN model also uses a masked strategy to implement the image reconstruction for reducing the dependence on the labeled image. We validated the superiority of the ViTDFNN on a real sleep dataset. The scores of the ViTDFNN model under the F1-Score, Recall and Accuracy metrics are 0.994, 0.992 and 0.994, respectively.
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Published: 01 January 2025
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