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

Olsen, Anders Vinther; Stephansen, Jens B.; Leary, Eileen; Peppard, Paul E.; Sheungshul, Hong; Jennum, Poul; Sørensen, Helge Bjarup Dissing; Mignot, Emmanuel

doi:10.1016/j.jneumeth.2017.02.004

Cited by 16 publications

(8 citation statements)

References 20 publications

(34 reference statements)

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“…Additionally, the MSLT has poor test-retest reliability. 57 PSG evaluation with linear dimension analysis 58 and deep learning 59 has demonstrated the capability of ML models to objectively confirm narcolepsy with accuracy similar to traditional PSG followed by MSLT. ML tools might also be applied to signals acquired during MSLT to assist with diagnosis and reveal previously unseen subgroups within the central disorders of hypersomnolence.…”

Section: Improved Diagnosis and Subtyping Of Disordersmentioning

confidence: 99%

Artificial intelligence in sleep medicine: background and implications for clinicians

Goldstein

Berry

Kent

et al. 2020

Journal of Clinical Sleep Medicine

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Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

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Section: Improved Diagnosis and Subtyping Of Disordersmentioning

confidence: 99%

Artificial intelligence in sleep medicine: background and implications for clinicians

Goldstein

Berry

Kent

et al. 2020

Journal of Clinical Sleep Medicine

View full text Add to dashboard Cite

show abstract

“…After development of an automatic classifier capable of separating sleep and wakefulness epochs with single channel EEG, individuals with narcolepsy with cataplexy were observed to have significantly more sleep–wake transitions during night than patients with narcolepsy without cataplexy and normal controls [ 62 ]. In subsequent work, Olsen et al used a linear discriminant analysis (LDA) model which utilized 38 features from EOG, EMG, and EEG to identify features that differentiated wake, stage N1, N2, N3, and REM sleep in control subjects [ 63 ]. Next, the derived 2-dimensional sleep state space projection was used to distinguish patients with narcolepsy, type I from controls by leveraging the known sleep state dissociation in narcolepsy patients.…”

Section: Other Use Cases For Artificial Intelligence and Sleep Medicinementioning

confidence: 99%

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Bandyopadhyay

Goldstein

2022

Sleep Breath

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Background The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. Method The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Results Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Conclusion Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

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“…Dissociated states where the distinction of REM and NREM is difficult or impossible are well described (Mahowald and Schenck, 1991; Vetrugno et al, 2009;Vetrugno and Montagna, 2011;Abgrall et al, 2015;Antelmi et al, 2016). Blurring of sleep states have been described in narcolepsy, where state instability is an important pathology (Diniz Behn et al, 2010;Olsen et al, 2017). Our data adds respiratory patterns to this analysis, and show that such transitional states, where there are features of coexisting REM and NREM sleep, are relatively common.…”

Section: Discussionmentioning

confidence: 55%

Multicomponent Analysis of Sleep Using Electrocortical, Respiratory, Autonomic and Hemodynamic Signals Reveals Distinct Features of Stable and Unstable NREM and REM Sleep

et al. 2020

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A new concept of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep is proposed, that of multi-component integrative states that define stable and unstable sleep, respectively, NREMS, NREMUS REMS, and REMUS. Three complementary data sets are used: obstructive sleep apnea (20), healthy subjects (11), and high loop gain sleep apnea (50). We use polysomnography (PSG) with beat-to-beat blood pressure monitoring, and electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC) analysis to demonstrate a bimodal, rather than graded, characteristic of NREM sleep. Stable NREM (NREMS) is characterized by high probability of occurrence of the <1 Hz slow oscillation, high delta power, stable breathing, blood pressure dipping, strong sinus arrhythmia and vagal dominance, and high frequency CPC. Conversely, unstable NREM (NREMUS) has the opposite features: a fragmented and discontinuous <1 Hz slow oscillation, non-dipping of blood pressure, unstable respiration, cyclic variation in heart rate, and low frequency CPC. The dimension of NREM stability raises the possibility of a comprehensive integrated multicomponent network model of NREM sleep which captures sleep onset (e.g., ventrolateral preoptic area-based sleep switch) processes, synaptic homeostatic delta power kinetics, and the interaction of global and local sleep processes as reflected in the spatiotemporal evolution of cortical “UP” and “DOWN” states, while incorporating the complex dynamics of autonomic-respiratory-hemodynamic systems during sleep. Bimodality of REM sleep is harder to discern in health. However, individuals with combined obstructive and central sleep apnea allows ready recognition of REMS and REMUS (stable and unstable REM sleep, respectively), especially when there is a discordance of respiratory patterns in relation to conventional stage of sleep.

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

Abstract: 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.

Cited by 16 publications

References 20 publications

Artificial intelligence in sleep medicine: background and implications for clinicians

Artificial intelligence in sleep medicine: background and implications for clinicians

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

Multicomponent Analysis of Sleep Using Electrocortical, Respiratory, Autonomic and Hemodynamic Signals Reveals Distinct Features of Stable and Unstable NREM and REM Sleep

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