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Introduction In a recent analysis of PSG studies with six, nine and twelve human scorers, we showed that sleep stage ambiguity is the rule rather than the exception, and that sleep stage probabilities (SSPs) calculated with artificial intelligence provide an excellent estimate of this ambiguity. Here we investigate clinical and demographic factors driving this ambiguity and evaluate potential benefits of using hypnodensity in addition to the traditional hypnogram for sleep assessment. Methods SSPs were determined in 195 healthy subjects aged 20–95 years and in 49 apnea patients aged 29–73 years (AHI: 5.8–105.5 events/hour). In addition to the recommended sleep parameters, we derived the percentage of ambiguous sleep stages (SSP ≤ 0.95), the mean amount of ambiguity per sleep stage (1-SSP), sleep stage continuity (absolute difference in SSP between two epochs) and sleep stage stability (two adjacent epochs belonging to the same sleep stage with SSP > 0.95 each) as well as NREM sleep depth (weighted average of NREM SSPs). Results The percentages of ambiguous NREM epochs (±SD) increased significantly with age (Pearson’s r=0.72; p< 0.01) from 40±9% in young healthy subjects (20-39 years, n=61) to 50±9% in middle-aged healthy subjects (40-59 years, n=59) and to 62±11% in older healthy subjects (≥ 60 years, n=75). The percentages of ambiguous REM epochs did not change significantly across age (r=0.1). Apnea patients showed an increased NREM ambiguity (77±15% compared to 53±10% in age- and sex-matched healthy controls; p< 0.01), while REM ambiguity was only slightly increased (p< 0.05). Furthermore, sleep stage continuity, stability, and NREM sleep depth decrease significantly with age and are significantly lower in apnea patients than in healthy controls (p< 0.01). Conclusion Artificial intelligence-based autoscoring shows ambiguity in 40% of NREM epochs in young healthy subjects, increasing to 77% in apnea patients. Assigning a single sleep stage to each epoch and presenting sleep architecture as a traditional hypnogram may be misleading, especially for older subjects and patients with sleep-disordered breathing. A hypnodensity chart representing sleep stage probabilities reflects this sleep staging ambiguity, provides all the information contained in a hypnogram, and offers insights into sleep ambiguity, continuity, stability, and sleep depth. Support (if any)

Study Objectives To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. Methods We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6–12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. Results The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). Conclusions Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

Introduction There have been significant advances in machine learning in recent years. This means that powerful methods are now available for classification problems, such as scoring sleep stages from neurological or cardiorespiratory signals. In the present work, validation studies for both applications are presented. Methods To determine the 5 sleep stages from the neurological signals, 54 sleep-wake-related features were calculated and classified by a bidirectional long short-term memory (LSTM) network which had been trained on 1956 manual scorings of 588 PSGs from 294 subjects (supervised deep learning). To determine the 4 stages (wake, light sleep, deep sleep, REM) from cardiorespiratory signals, a convolutional neural network combined with LSTM layers was used for feature extraction and classification. This network had been trained on 685 PSGs from 391 subjects (Bakker et al. JCSM 2021). The networks obtained were validated in 428 PSGs with one and 10 PSGs with 12 manual scorings (neurological staging) as well as in 2 two datasets, each containing 296 ambulatory recordings (cardiorespiratory staging). Results Cohen’s kappa between autoscoring based on neurological signals and manual scoring was 0.74 (95%-confidence interval: 0.74-0.74) for the 428 PSGs. The intraclass correlation coefficient (ICC) for absolute agreement between autoscoring and manual scoring was for the AHI 0.97 (0.96-0.98), for the arousal index 0.79 (0.67-0.86) and for the PLMSI 0.91 (0.88-0.93). The ICC between the sleep stage probabilities derived from the 12 manual scorings and the artificial intelligence (AI) derived hypnodensity was 0.91 (0.91-0.91). Cohen’s kappa values for the cardiorespiratory sleep staging were 0.68 (0.68-0.68) and 0.64 (0.63-0.64) for the 2 datasets with 296 ambulatory recordings each. Conclusion All metrics from the PSG validation studies show substantial (Cohen’s kappa > 0.6) as well as good to excellent agreement (ICC > 0.75 or > 0.90) compared to manual scorings. As an added value of the AI-supported PSG evaluation, the probabilities of the sleep stages per epoch are determined (hypnodensity graph). The valid estimation of the sleep stages from cardiorespiratory signals by means of AI may result in improved clinical interpretation of home sleep apnea tests, which are increasingly used in the sleep-disordered breathing diagnostic pathway. Support (If Any)

Study Objectives: The study compared adaptation responses and sleep pattern differences shown by normal sleepers and insomnia sufferers during lab (LPSG) and home (HPSG) polysomnography. Design: A counter-balanc ed, matched-group design was used. Participants underwent 3 consecutive nocturnal LPSG’s and 3 consecutive nocturnal PSG’s in their homes (HPSG’s). Setting: The sleep disorders laboratories at affiliated VA and university medical centers. Participants: Thirty-five (18 women) middle-aged (40 to 59 years) noncomplaining normal sleepers and an age-matched sample of 33 (17 women) individuals who met structured interview criteria for persistent primary insomnia were the study participants. Measurements and Results: A series of multivariate and univariate analyses were conducted with 9 common sleep parameters to address study objectives. Bed partner influences were controlled by conducting separate sets of analyses for those with and without routine home bed partners. The interaction of participant type (normal vs. insomnia), sleep setting, and PSG sequence (HPSG 1st vs. LPSG 1st) affected first night values of sleep efficiency and stage 2 sleep among those without routine bed partners, and REM latency and sleep efficiency among those with routine bed partners. Analyses which controlled for first night and sequencing effects showed a significant participant type x sleep setting interaction among those with bed partners. These latter analyses suggested that LPSG’s may underestimate the home sleep time of insomnia sufferers and overestimate the sleep continuity of normal sleepers, at least among those who routinely sleep with a bed partner. Conclusions: The nocturnal recording site may influence adaptation effects and sleep pattern differences noted between insomnia sufferers and normal sleepers.

Summary: Complaints of daytime dysfunction are common among chronic insomniacs, but laboratory compari­ sons of insomniacs and age-matched and gender-matched normal controls have generally failed to document these complaints. However, a few studies, which allowed subjects to sleep in their homes on the nights before daytime testing, have shown some relative diurnal deficits among insomniacs. The current study compared the effects of nocturnal laboratory and home polysomnogram (PSG) studies on subsequent daytime test results among older insomniacs and normal sleepers. Insomniacs (n = 32) and normal sleepers (n = 32) were randomly assigned to first undergo three nights of nocturnal PSG monitoring either in the sleep laboratory (16 insomniacs, 16 normal sleepers) or in their homes (16 insomniacs, 16 normal sleepers). Following the third night of PSG monitoring, subjects spent I day in the sleep laboratory, where they completed a four-trial multiple sleep latency test along with four trials of a computer-administered performance test battery. Results showed that insomniacs, as a group, were slightly, albeit consistently, sleepier than were normal sleepers following nights of home sleep monitoring, but a reverse of this trend was found among subjects who underwent nocturnal laboratory PSG before daytime testing. Furthermore, normal sleepers showed faster reaction times on a signal detection task than did insomniacs within the subgroup who underwent home PSGs prior to such testing. However, within the subgroup that underwent nocturnal laboratory PSGs, insomniacs' signal detection reaction times were significantly faster than those shown by normal sleepers. Results provide some support for the speculation that the nocturnal PSG monitoring site, used as a precursor to daytime testing, may systematically affect daytime comparisons between insomniacs and matched