Narcolepsy Diagnosis With Sleep Stage Features Using PSG Recordings

被引:4
|
作者
Wang, Jiquan [1 ,2 ]
Zhao, Sha [1 ,2 ]
Zhou, Yangxuan [2 ,3 ]
Jiang, Haiteng [2 ,4 ,5 ]
Yu, Zhenghe [6 ]
Li, Tao [2 ,4 ,5 ]
Li, Shijian [7 ,8 ,9 ]
Pan, Gang [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310013, Zhejiang, Peoples R China
[2] Zhejiang Univ, State Key Lab Brain Machine Intelligence, Hangzhou 311121, Zhejiang, Peoples R China
[3] Zhejiang Univ, Sch Software Technol, Ningbo 315103, Zhejiang, Peoples R China
[4] Zhejiang Univ, Hangzhou Peoples Hosp 7, Affiliated Mental Hlth Ctr, Dept Neurobiol,Sch Med, Hangzhou 310063, Zhejiang, Peoples R China
[5] MOE Frontier Sci Ctr Brain Sci & Brain Machine Int, Hangzhou 310063, Zhejiang, Peoples R China
[6] Zhejiang Univ, Hangzhou Peoples Hosp 7, Affiliated Mental Hlth Ctr, Dept Sleep Med,Sch Med, Hangzhou 310063, Zhejiang, Peoples R China
[7] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310013, Zhejiang, Peoples R China
[8] Qiantang Sci & Technol Innovat Ctr, Hangzhou 310018, Zhejiang, Peoples R China
[9] Zhejiang Univ, State Key Lab Brain Machine Intelligence, Hangzhou 311121, Zhejiang, Peoples R China
关键词
Sleep; Recording; Hospitals; Electromyography; Electroencephalography; Electrooculography; Nose; Narcolepsy diagnosis; sleep staging; PSG recordings; multi-task learning; NETWORKS;
D O I
10.1109/TNSRE.2023.3312396
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious public health problems. It is hard for doctors to correctly and objectively diagnose narcolepsy. Polysomnography (PSG) recordings, a gold standard for sleep monitoring and quality measurement, can provide abundant and objective cues for the narcolepsy diagnosis. There have been some studies on automatic narcolepsy diagnosis using PSG recordings. However, the sleep stage information, an important cue for narcolepsy diagnosis, has not been fully utilized. For example, some studies have not considered the sleep stage information to diagnose narcolepsy. Although some studies consider the sleep stage information, the stages are manually scored by experts, which is time-consuming and subjective. And the framework using sleep stages scored automatically for narcolepsy diagnosis is designed in a two-phase learning manner, where sleep staging in the first phase and diagnosis in the second phase, causing cumulative error and degrading the performance. To address these challenges, we propose a novel end-to-end framework for automatic narcolepsy diagnosis using PSG recordings. In particular, adopting the idea of multi-task learning, we take the sleep staging as our auxiliary task, and then combine the sleep stage related features with narcolepsy related features for our primary task of narcolepsy diagnosis. We collected a dataset of PSG recordings from 77 participants and evaluated our framework on it. Both of the sleep stage features and the end-to-end fashion contribute to diagnosis performance. Moreover, we do a comprehensive analysis on the relationship between sleep stages and narcolepsy, correlation of different channels, predictive ability of different sensing data, and diagnosis results in subject level.
引用
收藏
页码:3619 / 3629
页数:11
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