ArticleFrontiers in human neuroscience2016
Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 43 citations in OpenAlex.
- Clinical validation of a wireless patch-based polysomnography system.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Trial
- Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review.Biomimetics (Basel, Switzerland) · 2024Article
- Validation of spectral sleep scoring with polysomnography using forehead EEG device.Frontiers in sleep · 2024Article
- The Influence of Sleep Disturbance on Chronic Pain.Current pain and headache reports · 2022Review
- Unconscious mind activates central cardiovascular network and promotes adaptation to microgravity possibly anti-aging during 1-year-long spaceflight.Scientific reports · 2022Article
- Daily Variation in Sleep Quality is Associated With Health-Related Quality of Life in People With Spinal Cord Injury.Archives of physical medicine and rehabilitation · 2022Article
- Article
- Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace-A Pilot Study.International journal of environmental research and public health · 2021Article
- Amount of < 1Hz deep sleep correlates with melatonin dose in military veterans with PTSD.Neurobiology of sleep and circadian rhythms · 2021Article
- Sleep and PTSD in the Military Forces: A Reciprocal Relationship and a Psychiatric Approach.Brain sciences · 2021Review
- Soft Wireless Bioelectronics and Differential Electrodermal Activity for Home Sleep Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula.Scientific reports · 2020Article
- Effect of an Inflatable Air Mattress with Variable Rigidity on Sleep Quality.Sensors (Basel, Switzerland) · 2020Article
- Sleep quality prediction in caregivers using physiological signals.Computers in biology and medicine · 2019Article
- A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea.IEEE transactions on bio-medical engineering · 2018Article
- Cognitive and Physiologic Impacts of the Infraslow Oscillation.Frontiers in systems neuroscience · 2018Review
- In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep <1 Hz.Frontiers in human neuroscience · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1-1 Hz or 1-3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.