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ZHANG Rulin, LI Ruixue, LIANG Jiakai, et al., “Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-Snoring Sound Events,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.210, 2023.
Citation: ZHANG Rulin, LI Ruixue, LIANG Jiakai, et al., “Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-Snoring Sound Events,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.210, 2023.

Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-Snoring Sound Events

doi: 10.23919/cje.2022.00.210
Funds:  This work was supported by Zhejiang Key Research and Development Project (2022C01048) and Zhejiang Province Public Welfare Project (LGG22F010012).
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  • Author Bio:

    Rulin ZHANG received the B.E. degree from College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China, in 2019. She is currently pursuing the master’s degree in Electronics and Information College, Hangzhou Dianzi University, Hangzhou, China. Her research interests include spike neural networks and voice recognition. (Email: rlzhang_0609@163.com)

    Ruixue LI graduated from the School of Electrical and Information Engineering, Tianjin University and earned her Ph. D in 2020. She is now a post-doctor in Electronics and Information College, Hangzhou Dianzi University, Hangzhou, China. Her research interests include computational neuroscience, pattern recognition, audio analysis and image processing. (Email: rxli2012@163.com)

    Jiakai LIANG received his B.E. degree in electronic information engineering from the Wenzhou University, Wenzhou, China, in 2021. He is currently pursuing the master’s degree in Electronics and Information College, Hangzhou Dianzi University, Hangzhou, China. His research interests include spiking neural networks and image processing. (Email: ljk211040090@hdu.edu.cn)

    Keqiang YUE received the Ph. D from College of Electrical Engineering Zhejiang University, Hangzhou, China, in 2014. He is currently a master’s supervisor with Electronics and Information College, Hangzhou Dianzi University, Hangzhou, China. His research interest include internet of things system development and intelligent communication. (Email: kqyue@hdu.edu.cn)

    Wenjun LI (corresponding author) received the Ph. D from Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghia, Chian, in 2004. He is currently a professor of Hangzhou Dianzi University, Hangzhou, China. His research interest include integrated circuit design and intelligent computing and hardware. (Email: liwenjun@hdu.edu.cn)

    Yilin LI received the Ph. D from School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China, in 2018. He is currently teaching in Electronics and Information College, Hangzhou Dianzi University, Hangzhou, China. His research interests include multidisciplinary design optimization, weak signal detection and AI model compression. (Email: ericlee@hdu.edu.cn)

  • Received Date: 2022-07-10
  • Accepted Date: 2023-06-21
  • Available Online: 2023-08-25
  • Snoring is a widespread occurrence that impacts human sleep quality. It is also one of the earliest symptoms of many sleep disorders. Snoring is accurately detected, making further screening and diagnosis of sleep problems easier. However, snoring is frequently ignored because of its underrated and costly detection costs. As a result, this research offered an alternative method for snoring detection based on a Long Short-Term Memory based Spiking Neural Network (LSTM-SNN) that is appropriate for large-scale home detection for snoring. In this paper, We designed acquisition equipment to collect the sleep recordings of 54 subjects and constructed the sleep sound database in the home environment. And Mel Frequency Cepstral Coefficients (MFCCs) were extracted from these sound signals and encoded into spike trains by a threshold encoding approach. Then, they were classified automatically as non-snoring or snoring sounds by our LSTM-SNN model. We used the backpropagation algorithm based on an alternative gradient in the LSTM-SNN to complete the parameter update. The categorization percentage reached an impressive 93.4%, accompanied by a remarkable 36.9% reduction in computer power compared to the regular LSTM model.
  • 1BMI: Body Mass Index, calculated by the weight and hight.
    2AHI: Apnea Hypopnea Index, which clinically is used for OSAHS diagnosis.
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