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| A Study on Sleep Staging Algorithm for Patients with Sleep Apnea Syndrome |
| Lu Keke1,2,3, Qi Xia1,2,3, Zhang Jianbao1,2,3, Wang Gang1,2,3#*, Yan Xiangguo 1,2,3#* |
1(Institute of Health and Rehabilitation Science, School of Life Science and Technology,The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an 710049, China)
2(National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China)
3(The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China) |
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Abstract Aiming at the lack of effective automatic sleep staging method for wearable sleep monitoring, an automatic sleep staging algorithm suitable for patients with sleep apnea syndrome was proposed in this paper. Through the R-R interval sequence of an ECG signal, the signals of heart rate variability (HRV), respiratory amplitude variability (RAV) and respiratory rate variability (RRV) were obtained. On this basis, 55 features of time, frequency, and nonlinear domains were extracted. Four sleep staging models with different classification granularity were constructed by using gated circulation unit network: W-S two classification, W-REM-NREM three classification, W-REM-LS-SWS four classification, W-REM-N1-N2-N3 five classification. A categorical weighting method for the loss function was used to effectively reduce the impact of data imbalance on the sleep staging results. The accuracy, Cohen's kappa coefficient and sleep structure indexes were used to evaluate the performance of the sleep staging algorithm. The results showed that the values of accuracy and kappa of the four classifiers were 85.06%, 75.44%, 63.80%, 62.13%, and 0.54, 0.49, 0.41, 0.41, respectively, and the sleep structure analyzing showed there was no statistically significant difference from clinical results. The method proposed displayed the ability of meeting the needs of sleep quality evaluation and is expected to be used for wearable sleep monitoring applications.
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Received: 17 September 2020
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Corresponding Authors:
*E-mail: ggwang@xjtu.edu.cn ; xgyan@mail.xjtu.edu.cn
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