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Qian X, Qiu Y, He Q, Lu Y, Lin H, Xu F, Zhu F, Liu Z, Li X, Cao Y, Shuai J. A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals. Brain Sci 2021; 11:1274. [PMID: 34679339 PMCID: PMC8533904 DOI: 10.3390/brainsci11101274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/20/2021] [Accepted: 08/24/2021] [Indexed: 11/16/2022] Open
Abstract
Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.
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Affiliation(s)
- Xiangyu Qian
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Ye Qiu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Qingzu He
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Yuer Lu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Hai Lin
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Fei Xu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Fangfang Zhu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Zhilong Liu
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
| | - Yuping Cao
- Department of Psychiatry of Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Jianwei Shuai
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; (X.Q.); (Y.Q.); (Q.H.); (Y.L.); (H.L.); (F.X.); (F.Z.); (Z.L.); (X.L.)
- National Institute for Data Science in Health and Medicine, and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China
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