| Autor: |
Wendong Xiao, Xinghao Wu, Wangqilin Zhao |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Communications in Computer and Information Science ISBN: 9789811519246 |
| DOI: |
10.1007/978-981-15-1925-3_38 |
| Popis: |
Sleep is a physiological process controlled by the autonomic nervous system. Autonomic activity differs in different stages of sleep. Heart Rate Variability (HRV) is a widely recognized indicator of autonomic activity and has been commonly used in sleep stage classification. However, HRV suffers from low repeatability and is very volatile. Cardiopulmonary Coupling (CPC) reflects autonomic activity from a different perspective and has been used to measure sleep quality. This paper explores the effect of using combination of HRV and CPC features in sleep stage classification. The experimental results using a decision-tree-based support vector machine (DTB-SVM) classifier on MIT-BIH polysomnographic database have shown that by adding three CPC features, the overall sleep stage classification accuracy has been raised from 95.74% (Kappa = 0.9257) to 96.89% (Kappa = 0.9449). The CPC features have shown to be superior in distinguishing deep sleep stage (with 3.69% increase). The classification accuracy of wake and light sleep has also improved with 1.67% and 0.89%, respectively. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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