Altered Sleep Stage Transitions of REM Sleep: A Novel and Stable Biomarker of Narcolepsy

Narcolepsy and rapid eye movement sleep

Since the first description of narcolepsy at the end of the 19th Century, great progress has been made. The disease is nowadays distinguished as narcolepsy type 1 and type 2. In the 1960s, the discovery of rapid eye movement sleep at sleep onset led to improved understanding of core sleep-related disease symptoms of the disease (excessive daytime sleepiness with early occurrence of rapid eye movement sleep, sleep-related hallucinations, sleep paralysis, rapid eye movement parasomnia), as possible dysregulation of rapid eye movement sleep, and cataplexy resembling an intrusion of rapid eye movement atonia during wake. The relevance of non-sleep-related symptoms, such as obesity, precocious puberty, psychiatric and cardiovascular morbidities, has subsequently been recognized. The diagnostic tools have been improved, but sleep-onset rapid eye movement periods on polysomnography and Multiple Sleep Latency Test remain key criteria. The pathogenic mechanisms of narcolepsy type 1 have been partly elucidated after the discovery of strong HLA class II association and orexin/hypocretin deficiency, a neurotransmitter that is involved in altered rapid eye movement sleep regulation. Conversely, the causes of narcolepsy type 2, where cataplexy and orexin deficiency are absent, remain unknown. Symptomatic medications to treat patients with narcolepsy have been developed, and management has been codified with guidelines, until the recent promising orexin-receptor agonists. The present review retraces the steps of the research on narcolepsy that linked the features of the disease with rapid eye movement sleep abnormality, and those that do not appear associated with rapid eye movement sleep.

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review

Purpose

This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses.

Patients and Methods

A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician.

Results

U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement.

Conclusion

Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.

Sleep Stage Transitions and Sleep-Dependent Memory Consolidation in Children with Narcolepsy-Cataplexy

Electroencephalographic sleep stage transitions and altered first REM sleep period transitions have been identified as biomarkers of type 1 narcolepsy in adults, but not in children. Studies on memory complaints in narcolepsy have not yet investigated sleep-dependent memory consolidation. We aimed to explore stage transitions; more specifically altered REM sleep transition and its relationship with sleep-dependent memory consolidation in children with narcolepsy. Twenty-one children with narcolepsy-cataplexy and twenty-three healthy control children completed overnight polysomnography and sleep-dependent memory consolidation tests. Overnight transition rates (number of transitions per hour), global relative transition frequencies (number of transitions between a stage and all other stages/total number of transitions × 100), overnight transitions to REM sleep (transition from a given stage to REM/total REM transitions × 100), and altered first REM sleep period transitions (transitions from wake or N1 to the first REM period) were computed. Narcoleptic children had a significantly higher overnight transition rate with a higher global relative transition frequencies to wake. A lower sleep-dependent memory consolidation score found in children with narcolepsy was associated with a higher overnight transition frequency. As observed in narcoleptic adults, 90.48% of narcoleptic children exhibited an altered first REM sleep transition. As in adults, the altered sleep stage transition is also present in children with narcolepsy-cataplexy, and a higher transition rate could have an impact on sleep-dependent memory consolidation. These potential biomarkers could help diagnose type 1 narcolepsy in children more quickly; however, further studies with larger cohorts, including of those with type 2 narcolepsy and hypersomnia, are needed.

Novel Objective Measures of Hypersomnolence

Purpose of review

To provide a brief overview of current objective measures of hypersomnolence, discuss proposed measure modifications, and review emerging measures.

Recent findings

There is potential to optimize current tools using novel metrics. High-density and quantitative EEG-based measures may provide discriminative informative. Cognitive testing may quantify cognitive dysfunction common to hypersomnia disorders, particularly in attention, and objectively measure pathologic sleep inertia. Structural and functional neuroimaging studies in narcolepsy type 1 have shown considerable variability but so far implicate both hypothalamic and extra-hypothalamic regions; fewer studies of other CDH have been performed. There is recent renewed interest in pupillometry as a measure of alertness in the evaluation of hypersomnolence.

Summary

No single test captures the full spectrum of disorders and use of multiple measures will likely improve diagnostic precision. Research is needed to identify novel measures and disease-specific biomarkers, and to define combinations of measures optimal for CDH diagnosis.

Muscle atonia index during multiple sleep latency test: A possible marker to differentiate narcolepsy from other hypersomnias

OBJECTIVE

The complexity and delay of the diagnosis of narcolepsy require several diagnostic tests and invasive procedures such as lumbar puncture. Our study aimed to determine the changes in muscle tone (atonia index, AI) at different levels of vigilance during the entire multiple sleep latency test (MSLT) and each nap in people with narcolepsy type 1 (NT1) and 2 (NT2) compared with other hypersomnias and its potential diagnostic value.

METHODS

Twenty-nine patients with NT1 (11 M 18F, mean age 34.9 years, SD 16.8) and sixteen with NT2 (10 M 6F, mean age 39 years, SD 11.8) and 20 controls with other hypersomnias (10 M, 10F mean age 45.1 years, SD 15.1) were recruited. AI was evaluated at different levels of vigilance (Wake and REM sleep) in each nap and throughout the MSLT of each group. The validity of AI in identifying patients with narcolepsy (NT1 and NT2) was analyzed using receiver operating characteristic (ROC) curves.

RESULTS

AI during wakefulness (WAI) was significantly higher in the narcolepsy groups (NT1 and NT2 p < 0.001) compared to the hypersomniac group. AI during REM sleep (RAI) (p = 0.03) and WAI in nap with sudden onsets of REM sleep periods (SOREMP) (p = 0.001) were lower in NT1 than in NT2. The ROC curves showed high AUC values for WAI (NT1 0.88; Best Cut-off > 0.57, Sensitivity 79.3 % Specificity 90 %; NT2 0.89 Best Cut-off > 0.67 Sensitivity 87.5 % Specificity 95 %; NT1 and NT2 0.88 Best Cut-off > 0.57 Sensitivity 82.2 % Specificity 90 %) in discriminating subjects suffering from other hypersomnias. RAI and WAI in nap with SOREMP showed a poor AUC value (RAI AUC: 0.7 Best cutoff 0.7 Sensitivity 50 % Specificity 87.5 %; WAI in nap before SOREMP AUC: 0.66, Best cut-off < 0.82 sensitivity 61.9 % and specificity 67.35 %) differentiating NT1 and NT2.

CONCLUSIONS

WAI may represent an encouraging electrophysiological marker of narcolepsy and suggests a vulnerable tendency to dissociative wake / sleep dysregulation lacking in other forms of hypersomnia.

SIGNIFICANCE

AI during wakefulness may help distinguish between narcolepsy and other hypersomnias.

Temporal distribution of sleep onset REM periods and N3 sleep in the MSLT and night polysomnogram of narcolepsy type 1 and other hypersomnias

INTRODUCTION

The presence of ≥2 sleep onset REM periods (SOREMP) in the Multiple Sleep Latency Test (MSLT) and the previous night polysomnogram (PSG) is part of the diagnostic criteria of narcolepsy, with every SOREMP having the same diagnostic value, despite evidence suggesting that time of SOREMP appearance and their preceding sleep stage might be relevant. We studied the temporal distribution of SOREMPs and associated sleep stages in the MSLT of patients with narcolepsy type 1 (NT1) and other hypersomnias (OH).

METHODS

We reviewed consecutive five-nap MSLTs and their preceding PSG from 83 untreated adult patients with hypersomnolence and ≥1 SOREMPs. Wake/N1(W/N1)-SOREMPs, N2-SOREMPs, and N3 sleep presence and time of appearance were analyzed.

RESULTS

Thirty-nine patients had NT1 and 44 OH. There were 183 (78%) SOREMPs in patients with NT1 and 83 (31%) in OH. Sixty-seven percent of SOREMPs in NT1 were from W/N1, and 20% -none from wake-in OH (p < 0.001). Most patients (94%) with ≥2 W/N1-SOREMPs had NT1 (specificity 95%, sensitivity 82%). In patients with NT1 but not in OH, W/N1-SOREMPs decreased throughout the day (from 79% in the 1st nap to 33% in the preceding night, p < 0.001), whereas N2-SOREMPs did not change. N3 sleep frequency in the 5th nap was higher in NT1 than in OH (28% vs. 7%, p:0.009). Nocturnal-SOREMP plus ≥4 daytime SOREMPs, Wake-REM transitions, and REM followed by N3 were only seen in NT1.

CONCLUSION

Measuring the sleep stage sequence and temporal distribution of SOREMP helps to identify patients with narcolepsy in the MSLT.

Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography

Automatic algorithms are a proposed alternative to manual assessment of polysomnography data for analyzing sleep structure; however, none are acceptably accurate for clinical use. We investigated the feasibility of an automated sleep stage scoring system called Sleep Scope, which is intended for use with portable 1-channel electroencephalograph, and compared it with the traditional polysomnography scoring method. Twenty-six outpatients and fourteen healthy volunteers underwent Sleep Scope and polysomnography assessments simultaneously. Polysomnography records were manually scored by three sleep experts. Sleep Scope records were scored using a dedicated auto-staging algorithm. Sleep parameters, including total sleep time, sleep latency, wake after sleep onset, and sleep efficiency, were calculated. The epoch-by-epoch pairwise concordance based on the classification of sleep into five stages (i.e., wake, rapid eye movement, N1, N2, and N3) was also evaluated after validating homogeneity and bias between Sleep Scope and polysomnography. Compared with polysomnography, Sleep Scope seemed to overestimate sleep latency by approximately 3 min, but there was no consistent tendency in bias in other sleep parameters. The Κ values ranged from 0.66 to 0.75 for experts' inter-rater polysomnography scores and from 0.62 to 0.67 for Sleep Scope versus polysomnography scores, which indicated sufficient agreement in the determination of sleep stages based on the Landis and Koch criteria. We observed sufficient concordance between Sleep Scope and polysomnography despite lower concordance in sleep disorder patients. Thus, this auto-staging system might serve as a novel clinical tool for reducing the time and expenses required of medical staff and patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00421-5.

Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model

Objective.Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT.Approach.We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from our hospital, considering ten patients with narcolepsy and fourteen patients with MSLT negative.Main results.Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population.Significance.This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.

Low carnitine palmitoyltransferase 1 activity is a risk factor for narcolepsy type 1 and other hypersomnia

Study Objectives

Narcolepsy type 1 (NT1) is associated with metabolic abnormalities but their etiology remains largely unknown. The gene for carnitine palmitoyltransferase 1B (CPT1B) and abnormally low serum acylcarnitine levels have been linked to NT1. To elucidate the details of altered fatty acid metabolism, we determined levels of individual acylcarnitines and evaluated CPT1 activity in patients with NT1 and other hypersomnia.

Methods

Blood samples from 57 NT1, 51 other hypersomnia patients, and 61 healthy controls were analyzed. The levels of 25 major individual acylcarnitines were determined and the C0/(t[C16] + t[C18]) ratio was used as a CPT1 activity marker. We further performed transcriptome analysis using independent blood samples from 42 NT1 and 42 healthy controls to study the relevance of fatty acid metabolism. NT1-specific changes in CPT1 activity and in expression of related genes were investigated.

Results

CPT1 activity was lower in patients with NT1 (p = 0.00064) and other hypersomnia (p = 0.0014) than in controls. Regression analysis revealed that CPT1 activity was an independent risk factor for NT1 (OR: 1.68; p = 0.0031) and for other hypersomnia (OR: 1.64; p = 0.0042). There was a significant interaction between obesity (BMI &lt;25, ≥25) and the SNP rs5770917 status such that nonobese NT1 patients without risk allele had better CPT1 activity (p = 0.0089). The expression levels of carnitine-acylcarnitine translocase (CACT) and CPT2 in carnitine shuttle were lower in NT1 (p = 0.000051 and p = 0.00014, respectively).

Conclusions

These results provide evidences that abnormal fatty acid metabolism is involved in the pathophysiology of NT1 and other hypersomnia.

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series

The widely used guidelines for sleep staging were developed for the visual inspection of electrophysiological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time-series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states.

Absence of multiple sleep-onset rapid eye movement periods (SOREMPs) is not a specific feature of patients with pathological sleep prolongation

Purpose

Multiple sleep-onset rapid eye movement periods (SOREMPs) are involved in the pathophysiology of narcolepsy, but it is not clear whether the lack of multiple SOREMPs is associated with the pathophysiology of idiopathic hypersomnia or not. We examined the significance of multiple SOREMPs in patients with pathological sleep prolongation.

Methods

Participants were consecutive patients complaining of unexplained sleepiness and agreed to a 3-day-sleep studies; 24 h polysomnography (PSG) followed by standard PSG and multiple sleep latency test (MSLT). Forty-one (26 females, 21.9 ± 8.1 years old, BMI 20.4 ± 2.3 kg/m2) of 54 eligible patients without other sleep pathologies showed pathological sleep prolongation. We subdivided them into those with and without multiple SOREMPs on MSLT and compared clinical and PSG variables between groups.

Results

Six of 41 (14.6%) patients showed multiple SOREMPs on MSLT. There were almost no differences in sleep variables between those with and without multiple SOREMPs. We only found shorter mean sleep latency on MSLT and more REM cycles on 24 h PSG in those with multiple SOREMPs (adjusted p = 0.016 and 0.031). The frequencies of REM-related phenomena and clinical symptoms related to idiopathic hypersomnia were not different between groups.

Conclusion

Our results indicated that patients with pathological sleep prolongation had the same clinical profiles regardless of the status of SOREMPs, suggesting the absence of multiple SOREMPs, prerequisite for the diagnosis of idiopathic hypersomnia, is not a specific feature of pathological sleep prolongation. Confirmation of sleep prolongation alone could be a diagnostic tool for idiopathic hypersomnia.

Differential characteristics of repeated polysomnography and multiple sleep latency test parameters in narcolepsy type 1 and type 2 patients: a longitudinal retrospective study

PURPOSE

Narcolepsy is a chronic disorder and its phenotype is dichotomized into narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2). The clinical course and pathophysiological mechanisms of these two clinical entities and their differences are not adequately defined. This study aimed to explore the differential longitudinal patterns of polysomnography (PSG) and multiple sleep latency test (MSLT) in NT1 and NT2.

METHODS

In this retrospective study demographic characteristics, PSG, and MSLT parameters at baseline and follow-up were compared between NT1 and NT2 patients. Patients with both follow-up MSLT and PSG were selected for sub-group analysis. Baseline and follow-up MSLT and PSG parameters were compared.

RESULTS

Of 55 patients with narcolepsy, mean follow-up periods were 7.4 ± 3.5 years for NT1 and 5.5 ± 2.9 for NT2. Demographic data showed increased body mass index and prevalence of sleep paralysis in NT1. Baseline PSG characteristics between NT1 and NT2 showed decreased sleep latency (p = 0.016) and REM latency (p = 0.046) in NT1 group when compared with NT2. Nocturnal SOREMP on PSG was more prevalent in NT1 (p = 0.017), and half of NT2 patients with nocturnal SOREMP on PSG changed their diagnoses to NT1. On follow-up PSG, NT1 displayed reductions in sleep stage N2 (p = 0.006) and N3 (p = 0.048), while wake after sleep onset (WASO) (p = 0.023) and apnea-hypopnea index (AHI) (p = 0.007) were significantly increased.

CONCLUSION

Differential MSLT and PSG characteristics of NT1 and NT2 in at baseline and follow-up indicate that NT1 and NT2 are distinct disease phenotypes, and that they present with a contrasting course of disease.

Utility of the sleep stage sequence preceding sleep onset REM periods for the diagnosis of narcolepsy: a study in a Japanese cohort

BACKGROUND

The minimum narcolepsy criteria "mean sleep latency (MSL) ≤8 min and ≥2 sleep onset rapid eye movement (REM) periods (SOREMPs) on polysomnography (PSG) and the multiple sleep latency test (MSLT)," according to The International Classification of Sleep Disorders, Third Edition (ICSD-3), are not specific to narcolepsy. Recently, the characteristic sleep stage sequences preceding SOREMPs in narcolepsy have received attention, but their diagnostic utility remains unclear.

METHODS

We retrospectively reviewed PSG/MSLT records and chart data for 102 Japanese patients with hypersomnia and at least one SOREMP. We examined the sporadic rates of two sleep stage sequences preceding the SOREMPs-wakefulness or stage 1 to REM (W/S1→R) and stage 2 to REM (S2→R)-comparing these between patient groups with narcolepsy type 1 (N = 28), narcolepsy type 2 (N = 19), and other hypersomnia (N = 55). We also examined the utility of three simple indices using the occurrence of W/S1→R SOREMPs for distinguishing between narcolepsy and other hypersomnia in patients who satisfied the minimum narcolepsy criteria.

RESULTS

W/S1→R SOREMPs were significantly more frequent in narcolepsy than in other hypersomnia, and this tendency was also observed even in the patients who satisfied the minimum narcolepsy criteria. The three indices had moderate sensitivities and specificities for distinguishing between narcolepsy and other hypersomnia in patients satisfying the minimum narcolepsy criteria.

CONCLUSIONS

The W/S1→R pattern was observed significantly more frequently in narcolepsy than in other hypersomnia, suggesting it may help with differentiating narcolepsy from other hypersomnia in patients demonstrating the narcolepsy criteria, although its ability to do so may be modest.

Comparison of the macro and microstructure of sleep in a sample of sleep clinic hypersomnia cases

The purpose of this study was to elucidate the differentiating or grouping EEG characteristics in various hypersomnias (type 1 and type 2 narcolepsy (N-1 and N-2) and idiopathic hypersomnia (IH) compared to an age-matched snoring reference group (SR). Polysomnogram sleep EEG was decomposed into a 4-frequency state model. The IH group had higher sleep efficiency (SE; 92.3% vs. 85.8%; sp < 0.05), lower WASO (IH = 35.4 vs. N-1 = 65.5 min; p < 0.01), but similar (i.e. high) arousal indices as N-1 (~33/h). N-1 and N-2 had earlier REM latency than IH and SR (N-1 = 64.8, N-2 = 76.3 vs. IH/SR = 118 min, p < 0.05). N-1 and N-2 showed an increase in MF1 segments (characteristic of stage 1 and REM) across the night as well as distinct oscillations every 2 h, but MF1 segment timing was advanced by 30 min compared to the SR group (p < 0.05). This suggests the presence of circadian organization to sleep that is timed earlier or of increased pressure and/or lability. MF1 demonstrated a mixed phenotype in IH, with an early 1^st oscillation (like N-1 and N-2), 2^nd oscillation that overlapped with the SR group, and a surge prior to wake (higher than all groups). This phenotype may reflect a heterogeneous group of individuals, with some having more narcolepsy-like characteristics (i.e. REM) than others. LF domain (delta surrogate) was enhanced in IH and N-1 and more rapidly dissipated compared to N-2 and SR (p < 0.05). This suggests an intact homeostatic sleep pattern that is of higher need/reduced efficiency whereas rapid dissipation may be an underlying mechanism for sleep disruption.

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Low frequency sleep (delta surrogate) was enhanced in Idiopathic Hypersomnia and Type 1 Narcolepsy and rapidly dissipated across the sleep period.

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Type 1 and 2 Narcoleptics demonstrated a mixed frequency 1 phenotype (REM surrogate) consistent with intact circadian control and advanced timing.

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Idiopathic hypersomnia was characterized by a variable mixed frequency 1 phenotype (REM surrogate), suggesting some with more “narcolepsy-like” REM characteristics than others.

A false alarm of narcolepsy: obstructive sleep apnea masquerading as narcolepsy and depression

PURPOSE

We report a case with symptoms and signs of obstructive sleep apnea (OSA), depression, and narcolepsy. Polysomnographic (PSG) and multiple sleep latency test (MSLT) findings, clinical characteristics, and diagnostic challenges in this case are discussed.

METHODS

A 23-year-old single male presented with excessive daytime sleepiness, low mood, lack of energy, and snoring for 3 years. In addition, he reported excessive weight gain, lack of interest in work, partial loss of muscle tone during excitations, and sleep attacks during work and driving. He had experienced three episodes of sleep paralysis. The patient underwent a sleep study including PSG and MSLT.

RESULTS

On baseline PSG, he had an apnea/hypopnea index (AHI) of 72.8/h. The MSLT showed a mean sleep latency of 3.8 min and two sleep-onset rapid eye movement periods (SOREMPs). On admission, he had an Epworth Sleepiness Scale (ESS) score of 21, and positive findings for depression in the clinical interview and psychometric scales. He was treated with continuous positive airway pressure without any medication. Follow-up PSG and MSLT were performed after 1 week, which showed an AHI of 0/h without SOREMPs. After 1 month, there was no sign of depression.

CONCLUSIONS

This study reflects that OSA can present with cataplexy-like features and false positive MSLT results for narcolepsy, as well as depressive symptoms. The case highlights the complexity in which OSA can present to physicians, and emphasizes that clinicians should be aware that OSA can mimic narcolepsy and present with depressive symptoms.

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy

BACKGROUND

Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy.

NEW METHOD

A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane.

COMPARISON WITH EXISTING METHODS

This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods.

RESULTS

Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%.

CONCLUSION

Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.

Sleep-wake patterns, non-rapid eye movement, and rapid eye movement sleep cycles in teenage narcolepsy

BACKGROUND

To further characterize sleep disorders associated with narcolepsy, we assessed the sleep-wake patterns, rapid eye movement (REM), and non-REM (NREM) sleep cycles in Chinese teenagers with narcolepsy.

METHODS

A total of 14 Chinese type 1 narcoleptic patients (13.4 ± 2.6 years of age) and 14 healthy age- and sex-matched control subjects (13.6 ± 1.8 years of age) were recruited. Ambulatory 24-h polysomnography was recorded for two days, with test subjects adapting to the instruments on day one and the study data collection performed on day two.

RESULTS

Compared with the controls, the narcoleptic patients showed a 1.5-fold increase in total sleep time over 24 h, characterized by enhanced slow-wave sleep and REM sleep. Frequent sleep-wake transitions were identified in nocturnal sleep with all sleep stages switching to wakefulness, with more awakenings and time spent in wakefulness after sleep onset. Despite eight cases of narcolepsy with sleep onset REM periods at night, the mean duration of NREM-REM sleep cycle episode and the ratio of REM/NREM sleep between patients and controls were not significantly different.

CONCLUSION

Our study identified hypersomnia in teenage narcolepsy despite excessive daytime sleepiness. Sleep fragmentation extended to all sleep stages, indicating impaired sleep-wake cycles and instability of sleep stages. The limited effects on NREM-REM sleep cycles suggest the relative conservation of ultradian regulation of sleep.

Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy

OBJECTIVE

Type 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy.

METHODS

Sleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F(0.1) measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation.

RESULTS

(1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001).

CONCLUSIONS

Increased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.