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.