Multi-stage sleep classification using photoplethysmographic sensor. Issue 4 (12th April 2023)

• Record Type: Journal Article
• Authors: Motin, Mohammod Abdul; Karmakar, Chandan; Palaniswami, Marimuthu; Penzel, Thomas; Kumar, Dinesh
• Is Part Of: Royal Society open science. Volume 10:Issue 4(2023)
• Publication Date: 2023-04-12
• Subjects: multi-stage sleep classification -- wearable PPG signal -- cardio-respiratory biomarker -- arterial blood pressure -- pulse rate variability -- support vector machine; Science -- Periodicals; 500
• DOI: 10.1098/rsos.221517
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : The conventional approach to monitoring sleep stages requires placing multiple sensors on 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 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.