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As an approach of unobtrusive assessment of neonatal sleep state we aimed at an automated sleep state coding based only on heart rate variability obtained from electrocardiography used for regular patient monitoring. We analyzed active and quiet sleep states of preterm infants between 30 and 37weeks postmenstrual age. To determine the sleep states we used a nonlinear kernel support vector machine...
This preliminary study investigated the use of cardiac information or more specifically, heart rate variability (HRV), for automatic deep sleep detection throughout the night. The HRV data can be derived from cardiac signals, which were obtained from polysomnography (PSG) recordings. In total 42 features were extracted from the HRV data of 15 single-night PSG recordings (from 15 healthy subjects)...
This paper describes a method to adapt the spectral features extracted from heart rate variability (HRV) for sleep and wake classification. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Traditionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very...
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