Abstract
Sleep Apnea (SA) seriously affects human life and health. In recent years, many studies use polysomnography (PSG) to detect sleep apnea, but it is expensive and inconvenient. In order to solve this problem, this paper proposes a method to detect sleep apnea automatically by using a single Abdominal Respiratory Signal. In this method, Hilbert-Huang Transform (HHT) is used to extract frequency domain features, and combined with time domain features. Then sleep apnea is detected by machine learning methods such as Support Vector Machine, AdaBoosting and Random Forest (RF). The experimental results show that HHT can extract significant frequency domain features, and the accuracy of sleep apnea detection can reach 95% using Random Forest method. This method is better than the existing methods in the convenience and accuracy of detection. It is more suitable for family environment, and has a wide range of application prospects.
Supported by the Harbin science and technology bureau innovation under Grants No. 2017RAQXJ131, the Basic research project of scientific research operating expenses of Heilongjiang provincial colleges and universities under Grants No. KJCX201815, Heilongjiang Province Natural Science Foundation key project of China under Grant No. ZD2019F003.
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Lv, X., Li, J., Yan, Q. (2020). Automated Detection of Sleep Apnea from Abdominal Respiratory Signal Using Hilbert-Huang Transform. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_35
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DOI: https://doi.org/10.1007/978-3-030-57821-3_35
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