The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with

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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.

Study Objectives: The aim was to investigate how the severity of apneas, hypopneas, and related desaturations is associated with obstructive sleep apnea (OSA)-related daytime sleepiness. Methods: Multiple Sleep Latency Tests and polysomnographic recordings of 362 patients with OSA were retrospectively analyzed and novel diagnostic parameters (eg, obstruction severity and desaturation severity), incorporating severity of apneas, hypopneas, and desaturations, were computed. Conventional statistical analysis and multivariate analyses were utilized to investigate connection of apnea-hypopnea index (AHI), oxygen desaturation index (ODI), conventional hypoxemia parameters, and novel diagnostic parameters with mean daytime sleep latency (MSL). Results: In the whole population, 10% increase in values of desaturation severity (risk ratio = 2.01, P <.001), obstruction severity (risk ratio = 2.18, P <.001) and time below 90% saturation (t 90% ) (risk ratio = 2.05, P < .001) induced significantly higher risk of having mean daytime sleep latency ≤ 5 minutes compared to 10% increase in AHI (risk ratio = 1.63, P <.05). In severe OSA, desaturation severity had significantly (P <.02) stronger negative correlation (ρ = −.489, P <.001) with mean daytime sleep latency compared to AHI (ρ = −.402, P < 0.001) and ODI (ρ = −.393, P < .001). Based on general regression model, desaturation severity and male sex were the most significant factors predicting daytime sleep latency. Conclusions: Severity of sleep-related breathing cessations and desaturations is a stronger contributor to daytime sleepiness than AHI or ODI and therefore should be included in the diagnostics and severity assessment of OSA.