Differentiation of narcolepsy without cataplexy from idiopathic hypersomnia relies entirely upon the multiple sleep latency test (MSLT). However, the test-retest reliability for these central nervous system hypersomnias has never been determined. Patients with narcolepsy without cataplexy, idiopathic hypersomnia, and physiologic hypersomnia who underwent two diagnostic multiple sleep latency tests were identified retrospectively. Correlations between the mean sleep latencies on the two studies were evaluated, and we probed for demographic and clinical features associated with reproducibility versus change in diagnosis. Thirty-six patients (58% women, mean age 34 years) were included. Inter -test interval was 4.2 ± 3.8 years (range 2.5 months to 16.9 years). Mean sleep latencies on the first and second tests were 5.5 (± 3.7 SD) and 7.3 (± 3.9) minutes, respectively, with no significant correlation (r = 0.17, p = 0.31). A change in diagnosis occurred in 53% of patients, and was accounted for by a difference in the mean sleep latency (N = 15, 42%) or the number of sleep onset REM periods (N = 11, 31%). The only feature predictive of a diagnosis change was a history of hypnagogic or hypnopompic hallucinations. The multiple sleep latency test demonstrates poor test-retest reliability in a clinical population of patients with central nervous system hypersomnia evaluated in a tertiary referral center. Alternative diagnostic tools are needed.

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

The classification of the central disorders of hypersomnolence has undergone multiple iterations in an attempt to capture biologically meaningful disease entities in the absence of known pathophysiology. Accumulating data suggests that further refinements may be necessary. At the 7th International Symposium on Narcolepsy, a group of clinician-scientists evaluated data in support of keeping or changing classifications, and as a result suggest several changes. First, idiopathic hypersomnia with long sleep durations appears to be an identifiable and meaningful disease subtype. Second, idiopathic hypersomnia without long sleep time and narcolepsy without cataplexy share substantial phenotypic overlap and cannot reliably be distinguished with current testing, and so combining them into a single disease entity seems warranted at present. Moving forward, it is critical to phenotype patients across a wide variety of clinical and biological features, to aid in future refinements of disease classification.