Pei-Lin Lee1,2,3, Yi-Hao Huang4, Po-Chen Lin4, Yu-An Chiao4, Jen-Wen Hou4, Hsiang-Wen Liu2, Ya-Ling Huang2, Yu-Ting Liu5, Tzi-Dar Chiueh4,6. 1. Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan. 2. Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. 3. Center for Electronics Technology Integration, National Taiwan University. 4. Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan. 5. MediaTek Inc., Hsinchu, Taiwan. 6. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Abstract
STUDY OBJECTIVES: Reliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS). METHODS: The ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography. RESULTS: Mean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS. CONCLUSIONS: Our results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA.
STUDY OBJECTIVES: Reliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS). METHODS: The ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography. RESULTS: Mean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS. CONCLUSIONS: Our results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA.
Authors: Brendan P Lucey; Jennifer S Mcleland; Cristina D Toedebusch; Jill Boyd; John C Morris; Eric C Landsness; Kelvin Yamada; David M Holtzman Journal: J Sleep Res Date: 2016-06-02 Impact factor: 3.981
Authors: Shanguang Zhao; Fangfang Long; Xin Wei; Xiaoli Ni; Hui Wang; Bokun Wei Journal: Int J Environ Res Public Health Date: 2022-03-01 Impact factor: 3.390