Cataplexy example6/23/2023 ![]() For example, the abnormal breathing events that occur with obstructive sleep apnea (OSA) are generally less severe in N3 versus N2 because of central control of breathing changes, and they are more severe in REM sleep, due to upper airway muscle weakness 8. Although there is progression from N1 to N3 then to REM during the night, a process that repeats approximately every 90 min (the sleep cycle), each stage is associated with physiological changes that can be meaningful to the assessment of sleep disorders such as obstructive sleep apnea. PSG analysis is typically done by certified technicians who, through visual inspection on a standardized screen, assign a sleep stage to each 30 s segment of the full recording. N3 is characterized by a dominance of slow, high amplitude waves (>20%), while REM sleep is associated with low voltage, desynchronized EEG with occasional saw tooth waves, low muscle tone and REMs. N1 (sleep onset) is characterized by slowing of the EEG, disappearance of occipital alpha waves, decreased EMG and slow rolling eye movements, while N2 is associated with spindles and K-complexes. Each stage is characterized by different criteria, as defined by consensus rules published in the American Academy of Sleep Medicine (AASM) Scoring Manual 6, 7. When sleep is analyzed in PSGs, it is divided into discrete stages: wake, non-REM (NREM) sleep stage 1 (N1), 2 (N2) and 3 (N3), and REM. Sleep disorders are generally assessed at sleep clinics by performing sleep analysis using nocturnal polysomnography (PSG), a recording comprised of multiple digital signals which include electroencephalography (EEG), electrooculography (EOG), chin and leg electromyography (EMG), electrocardiography, breathing effort, oxygen saturation and airflow 6. The pathology also includes episodes of “sleep/wake dissociation” where the patient is half awake and half in REM sleep, for example, experiencing REM sleep muscle paralysis while awake (sleep paralysis, cataplexy) or dreaming while awake (hypnagogic hallucinations). ![]() Unique to narcolepsy is the extremely strong (97% versus 25%) association with a genetic marker, HLA-DQB1*06:02 5, and a well-characterized set of sleep disturbances that include short sleep latency, rapid transitions into REM sleep and poor nocturnal sleep consolidation. Typically beginning in childhood or adolescence, narcolepsy affects approximately 0.03% of the US, European, Korean and Chinese populations 4. ![]() This is reflected in the cerebrospinal fluid (CSF) concentrations of the hypocretin-1 (orexin-A) neuropeptide, where a concentration below 110 pg/ml is considered indicative of narcolepsy 2. There are ~90 different sleep disorders including insomnia (20% of population), obstructive and central sleep apnea (10%), restless legs syndrome (4%), rapid eye movement (REM) sleep behavior disorder (RBD) and hypersomnia syndromes such as type 1 narcolepsy (T1N) 2.Īmong these pathologies, T1N is unique as a disorder with a known, discrete pathophysiology-a destruction of hypocretin neurons in the hypothalamus likely of autoimmune origin 3. Sleep deprivation impairs performance, judgment and mood, and is a major preventable contributor to accidents 1. Sleep disorders and sleep dysregulation impact over 100 million Americans, contributing to medical consequences such as cardiovascular (arrhythmia, hypertension, stroke), metabolic (diabetes, obesity) and psychiatric disorders (depression, irritability, addictive behaviors). It also opens the possibility of diagnosing T1N using home sleep studies. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. The best model performed better than any individual scorer (87% versus consensus). ![]() Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. 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. 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.
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