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- W4280494973 abstract "It is estimated that 936 million adults aged 30–69 years suffer from mild to severe obstructive sleep apnea that can result in poor sleep quality and even endanger their lives. To facilitate the primary screening of sleep apnea in the home environment, a contactless and automated detection method of sleep apnea is proposed based on snoring signals and hybrid deep neural networks with real-time applications. In our work, 5700 snoring segments were collected from 32 patients. The Mel filter banks (FBanks) features were extracted from the snoring sounds and a classification model was constructed based on hybrid deep neural networks. By taking advantage of the pruning and quantization methods, the classification model was transplanted to an STM32 ARM embedded hardware platform with real-time application. Results showed that through the model pruning, with the tradeoff of losing 0.35% accuracy, the number of neural network parameters was reduced by 40.27%. When the pruned parameters were encapsulated, the speed of the convolution operation increased by 27.02%. In addition, the averaged classification accuracy of obstructive apnea snores, central apnea snore, hypopnea, and normal snores was 74.27%. The results demonstrate that our proposed contactless sleep apnea detection system, targeted for embedded hardware platform with real-time applications, is capable of identifying three kinds of apnea diseases and normal snores, which can play a major role in the primary screening and follow-up treatment of sleep apnea for in-home healthcare applications." @default.
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- W4280494973 date "2022-08-01" @default.
- W4280494973 modified "2023-10-04" @default.
- W4280494973 title "Contactless sleep apnea detection in snoring signals using hybrid deep neural networks targeted for embedded hardware platform with real-time applications" @default.
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- W4280494973 doi "https://doi.org/10.1016/j.bspc.2022.103765" @default.
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