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. 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. 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. 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.

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 suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ = 0.77) with a single frontal EEG channel and 83.9% (κ = 0.78) when supplemented with EOG. For the clinical dataset, the model achieved accuracies of 82.9% (κ = 0.77) and 83.8% (κ = 0.78) with a single EEG channel and two channels (EEG+EOG), respectively. The sleep staging accuracy decreased with increasing OSA severity. The single-channel accuracy ranged from 84.5% (κ = 0.79) for individuals without OSA diagnosis to 76.5% (κ = 0.68) for patients with severe OSA. In conclusion, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy and expectedly, the accuracy decreased with increasing OSA severity. Furthermore, the accuracies achieved in the public dataset were superior to previously published state-of-the-art methods. Adding an EOG channel did not significantly increase the accuracy. The automatic, single-channel-based sleep staging could enable easy, accurate, and cost-efficient integration of EEG recording into diagnostic ambulatory recordings.

Current diagnostic parameters estimating obstructive sleep apnoea (OSA) severity have a poor connection to the psychomotor vigilance of OSA patients. Thus, we aimed to investigate how the severity of apnoeas, hypopnoeas and intermittent hypoxaemia is associated with impaired vigilance.We retrospectively examined type I polysomnography data and corresponding psychomotor vigilance tasks (PVTs) of 743 consecutive OSA patients (apnoea–hypopnoea index (AHI) ≥5 events·h⁻¹). Conventional diagnostic parameters (e.g.AHI and oxygen desaturation index (ODI)) and novel parameters (e.g.desaturation severity and obstruction severity) incorporating duration of apnoeas and hypopnoeas as well as depth and duration of desaturations were assessed. Patients were grouped into quartiles based on PVT outcome variables. The odds of belonging to the worst-performing quartile were assessed. Analyses were performed for all PVT outcome variables using binomial logistic regression.A relative 10% increase in median depth of desaturations elevated the odds (OR_range1.20–1.37, p<0.05) of prolonged mean and median reaction times as well as increased lapse count. Similarly, an increase in desaturation severity (OR_range1.26–1.52, p<0.05) associated with prolonged median reaction time. Female sex (OR_range2.21–6.02, p<0.01), Epworth Sleepiness Scale score (OR_range1.05–1.07, p<0.01) and older age (OR_range1.01–1.05, p<0.05) were significant risk factors in all analyses. In contrast, increases in conventional AHI, ODI and arousal index were not associated with deteriorated PVT performance.These results show that our novel parameters describing the severity of intermittent hypoxaemia are significantly associated with increased risk of impaired PVT performance, whereas conventional OSA severity and sleep fragmentation metrics are not. These results underline the importance of developing the assessment of OSA severity beyond the AHI.

Objective: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events. The hypothesis was that the scoring and subsequent AHI calculation could be performed utilizing pulse oximetry data only. Methods: Polysomnography recordings of 877 individuals with suspected OSA were used to train the deep learning models. The same architecture was trained with three different input signal combinations (model 1: photoplethysmogram (PPG) and oxygen saturation (SpO $_{2}$ ); model 2: PPG, SpO $_{2}$ , and nasal pressure; model 3: SpO $_{2}$ , nasal pressure, electroencephalogram (EEG), oronasal thermocouple, and respiratory belts). Results: Model 1 reached comparative performance with models 2 and 3 for estimating the AHI (model 1 intraclass correlation coefficient (ICC) = 0.946; model 2 ICC = 0.931; model 3 ICC = 0.945), and REM-AHI (model 1 ICC = 0.912; model 2 ICC = 0.921; model 3 ICC = 0.883). The automatic sleep staging accuracies (wake/N1/N2/N3/REM) were 69%, 70%, and 79% with models 1, 2, and 3, respectively. Conclusion: AHI can be estimated using pulse oximetry-based automatic scoring. Explicit scoring of sleep stages and respiratory events allows visual validation of the automatic analysis, and provides information on OSA phenotypes. Significance: Automatic scoring of sleep stages and respiratory events with a simple pulse oximetry setup could allow cost-effective, large-scale screening of OSA.

To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal. A combination of convolutional and recurrent neural networks was used for PPG-based sleep staging. The models were trained using two large clinical datasets from Israel (n = 2149) and Australia (n = 877) and tested separately on three-class (wake/NREM/REM), four-class (wake/N1 + N2/N3/REM), and five-class (wake/N1/N2/N3/REM) classification. The relationship between OSA severity categories and sleep fragmentation was assessed using survival analysis of mean continuous sleep. Overlapping PPG epochs were applied to artificially obtain denser hypnograms for better identification of fragmented sleep. Automatic PPG-based sleep staging achieved an accuracy of 83.3% on three-class, 74.1% on four-class, and 68.7% on five-class models. The hazard ratios for decreased mean continuous sleep compared to the non-OSA group obtained with Cox proportional hazards models with 5-s epoch-to-epoch intervals were 1.70, 3.30, and 8.11 for mild, moderate, and severe OSA, respectively. With EEG-based hypnograms scored manually with conventional 30-s epoch-to-epoch intervals, the corresponding hazard ratios were 1.18, 1.78, and 2.90. PPG-based automatic sleep staging can be used to differentiate between OSA severity categories based on sleep continuity. The differences between the OSA severity categories become more apparent when a shorter epoch-to-epoch interval is used.

Summary: Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea–hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.