A Method of Detecting Sleep Apnea Using Random Forest

doi:10.13190/j.jbupt.2019-255

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (5): 64-70.doi: 10.13190/j.jbupt.2019-255

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A Method of Detecting Sleep Apnea Using Random Forest

Lü Xing-feng¹, LI Jin-bao²

1. School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China;
2. Qilu University of Technology(Shandong Academy of Sciences), Shandong Artificial Intelligence Institute, Jinan 250353, China

Received:2019-12-12 Published:2021-03-11

Abstract

Abstract: To solve the problem that various respiratory signals in polysomnography make the detection of sleep apnea complicated and affect patients' sleep, a method of automatic sleep apnea detection using random forest is proposed. The energy and marginal spectrum distribution of sleep apnea is significantly different from that of normal sleep after Hilbert-Huang transform. By extracting the relevant frequency domain features, combining with the time domain features, the random forest method in machine learning method is used to detect sleep apnea, which effectively reduces the detection complexity and improve the accuracy. Experiments show that this method is more convenient and accurate than the existing method, more suitable for home environment,and has a wide range of application prospects.

Key words: machine learning, sleep apnea, Hilbert-Huang transform, random forest

CLC Number:

TP391

Lü Xing-feng, LI Jin-bao. A Method of Detecting Sleep Apnea Using Random Forest[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 64-70.

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A Method of Detecting Sleep Apnea Using Random Forest

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