Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Issue 11 (21st May 2020)

• Record Type: Journal Article
• Authors: Korkalainen, Henri; Aakko, Juhani; Duce, Brett; Kainulainen, Samu; Leino, Akseli; Nikkonen, Sami; Afara, Isaac O; Myllymaa, Sami; Töyräs, Juha; Leppänen, Timo
• Is Part Of: Sleep. Volume 43:Issue 11(2020)
• Publication Date: 2020-05-21
• Subjects: deep learning -- photoplethysmogram -- obstructive sleep apnea -- recurrent neural networks -- sleep staging; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsaa098
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. Methods: PPG signals from the diagnostic polysomnographies of susptected OSA patients ( n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep. Results: The three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes. Conclusion: The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.