Multi-stage sleep classification using photoplethysmographic sensor

The conventional approach to monitoring sleep stages requires placing
multiple sensors on the patients, which is inconvenient for long-term
monitoring and requires expert support. We propose a single sensor
Photoplethysmographic (PPG) based automated multi-stage sleep
classification. This experimental study recorded the PPG during the entire
night's sleep of ten patients. Data analysis was performed to obtain
82 features from the recordings, which were then classified against the
sleep stages. The classification results using SVM with the polynomial
kernel gave the overall accuracy of 84.66%, 79.62%, and 72.23% for two,
three, and four-stage sleep classification. These results show that using
only PPG; it is possible to conduct sleep stage monitoring. These findings
open the opportunities for PPG-based wearable solutions for home-based
automated sleep monitoring.