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Multi-stage sleep classification using photoplethysmographic sensor.

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.

SUBMITTER: Motin MA

PROVIDER: S-EPMC10090868 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Multi-stage sleep classification using photoplethysmographic sensor.

Motin Mohammod Abdul MA Karmakar Chandan C Palaniswami Marimuthu M Penzel Thomas T Kumar Dinesh D

Royal Society open science 20230412 4

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 c ...[more]

PMID: 37063995

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