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Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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Abstract

Obstructive sleep apnea-hypopnea syndrome is a respiratory disorder characterized by abnormal breathing patterns during sleep. It causes problems during sleep, including loud snoring and frequent awaking. This study proposes a new approach for the detection of apnea-hypopnea events from the raw signal data of nasal airflow using convolutional neural networks. Convolutional neural networks are a prominent type of deep neural networks known for their ability to automatically learn features from high dimensional data without manual feature engineering. We demonstrate the applicability of this technique on a dataset of 24,480 samples (30 s long) extracted from nasal flow signals of 100 subjects in the MESA sleep study. The performance of the convolutional neural network model is compared with another approach that uses a support vector machine model with statistical features generated from the flow signal. Our results show that the convolutional neural network outperformed the support vector machine approach, achieving accuracy and F1-score of 75%.

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Acknowledgments

The MESA sleep dataset was supported by the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health. It is available through NHLBI National Sleep Research Resource at https://www.sleepdata.org/datasets/mesa. MESA Sleep was supported by contract NHLBI R01 L098433.

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Authors and Affiliations

  1. School of Information Technologies, The University of Sydney, Sydney, Australia

    Rim Haidar, Irena Koprinska & Bryn Jeffries

Authors
  1. Rim Haidar
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  2. Irena Koprinska
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  3. Bryn Jeffries
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Corresponding author

Correspondence to Rim Haidar .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Haidar, R., Koprinska, I., Jeffries, B. (2017). Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_83

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_83

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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