Sleep-wake stages classification based on heart rate variability

This paper presents a method aimed at classification of the sleep-wake stages using only the electrocardiogram (ECG) records. The feature extraction stage described in this paper was performed using method of Heart Rate Variability analysis (HRV). These features used in this study are based on QRS detection times. Therefore, this detection was generated automatically for all recordings using a new algorithm based on the detection of singularities through the local maxima in order to construct the RR series. We illustrate the performance of this method on an MIT/BIH Polysomnographic Database using Extreme learning machine (ELM).