Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based sleep staging to identify sleep stages with the traditional approach and by using overlapping 30-second epochs with 15-, 5-, 1-, or 0.5-second epoch-to-epoch duration. A dataset of 446 patients referred for polysomnography due to OSA suspicion was used to assess differences in the sleep architecture between OSA severity groups. The amount of wakefulness increased while REM and N3 decreased in severe OSA with shorter epoch-to-epoch duration. In other OSA severity groups, the amount of wake and N1 decreased while N3 increased. With the traditional 30-second epoch-to-epoch duration, only small differences in sleep continuity were observed between the OSA severity groups. With 1-second epoch-to-epoch duration, the hazard ratio illustrating the risk of fragmented sleep was 1.14 (p = 0.39) for mild OSA, 1.59 (p < 0.01) for moderate OSA, and 4.13 (p < 0.01) for severe OSA. With shorter epoch-to-epoch durations, total sleep time and sleep efficiency increased in the non-OSA group and decreased in severe OSA. In conclusion, more detailed sleep analysis emphasizes the highly fragmented sleep architecture in severe OSA patients which can be underestimated with traditional sleep staging. The results highlight the need for a more detailed analysis of sleep architecture when assessing sleep disorders.

Increased nocturnal arterial pulsation frequencies of obstructive sleep apnoea patients is associated with an increased number of lapses in a psychomotor vigilance task.

OBJECTIVES: Besides hypoxaemia severity, heart rate variability has been linked to cognitive decline in obstructive sleep apnoea (OSA) patients. Thus, our aim was to examine whether the frequency domain features of a nocturnal photoplethysmogram (PPG) can be linked to poor performance in the psychomotor vigilance task (PVT). METHODS: PPG signals from 567 suspected OSA patients, extracted from Type 1 diagnostic polysomnography, and corresponding results of PVT were retrospectively examined. The frequency content of complete PPGs was determined, and analyses were conducted separately for men (n=327) and women (n=240). Patients were grouped into PVT performance quartiles based on the number of lapses (reaction times ≥500 ms) and within-test variation in reaction times. The best-performing (Q1) and worst-performing (Q4) quartiles were compared due the lack of clinical thresholds in PVT. RESULTS: We found that the increase in arterial pulsation frequency (APF) in both men and women was associated with a higher number of lapses. Higher APF was also associated with higher within-test variation in men, but not in women. Median APF (ß=0.27, p=0.01), time spent under 90% saturation (ß=0.05, p<0.01), female sex (ß=1.29, p<0.01), older age (ß=0.03, p<0.01) and subjective sleepiness (ß=0.07, p<0.01) were significant predictors of belonging to Q4 based on lapses. Only female sex (ß=0.75, p<0.01) and depression (ß=0.91, p<0.02) were significant predictors of belonging to Q4 based on the within-test variation. CONCLUSIONS: In conclusion, increased APF in PPG provides a possible polysomnography indicator for deteriorated vigilance especially in male OSA patients. This finding highlights the connection between cardiorespiratory regulation, vigilance and OSA. However, our results indicate substantial sex-dependent differences that warrant further prospective studies.

Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness.

BACKGROUND: As nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features. METHODS: A total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO2), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15-35 mHz band in SpO2 (PSPO2) and HR (PHR), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data. RESULTS: SpO2 power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in PSPO2 (OR = 1.19-1.29) and PHR (OR = 1.81-1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS. CONCLUSIONS: We conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network.

A common symptom of obstructive sleep apnea (OSA) is excessive daytime sleepiness (EDS). The gold standard test for EDS is the multiple sleep latency test (MSLT). However, due to its high cost, MSLT is not routinely conducted for OSA patients and EDS is instead evaluated using sleep questionnaires. This is problematic however, since sleep questionnaires are subjective and correlate poorly with the MSLT. Therefore, new objective tools are needed for reliable evaluation of EDS. The aim of this study was to test our hypothesis that EDS can be estimated with neural network analysis of previous night polysomnographic signals. We trained a convolutional neural network (CNN) classifier using electroencephalography, electrooculography, and chin electromyography signals from 2,014 patients with suspected OSA. The CNN was trained to classify the patients into four sleepiness categories based on their mean sleep latency (MSL); severe (MSL < 5min), moderate (5 ≤ MSL < 10), mild (10 ≤ MSL < 15), and normal (MSL ≥ 15). The CNN classified patients to the four sleepiness categories with an overall accuracy of 60.6% and Cohen's kappa value of 0.464. In two-group classification scheme with sleepy (MSL < 10 min) and non-sleepy (MSL ≥ 10) patients, the CNN achieved an accuracy of 77.2%, with sensitivity of 76.5%, and specificity of 77.9%. Our results show that previous night's polysomnographic signals can be used for objective estimation of EDS with at least moderate accuracy. Since the diagnosis of OSA is currently confirmed by polysomnography, the classifier could be used simultaneously to get an objective estimate of the daytime sleepiness with minimal extra workload.