However, the research literature has distinguished EEG oscillations below and above 1 Hz that are hypothesized to originate in the cortex and thalamus, respectively ( Steriade et al., 1993). Standard sleep scoring guidelines do not differentiate between various frequencies of slow waves, as long as they are below 3 Hz. Therefore, it would be useful to include computational analysis to maximize observations that may not be obvious to even the most trained eye of a sleep technician. low-frequency spindles, or slow waves of variable frequency, can be difficult to detect and quantify. However, visual scoring in 30-s increments is limited since the human eye is minimally sensitive to slight variations in frequency. PSG provides a summary hypnogram of sleep architecture that is useful in sleep diagnosis and yields an inter-rater consistency of about 80% ( Danker-Hopfe et al., 2009). Heart rate typically drops with the onset of sleep ( Baust and Bohnert, 1969) loss of muscle tone and rapid eye movement (REM) are strong indicators of REM sleep. The gold standard for sleep scoring remains visual scoring of polysomnography (PSG), which includes EEG and heart rate, as well as muscle and eye movement data, among other measures ( Rechtschaffen and Kales, 1969). Unlike accelerometers, EEG detects not just lack of movement, but the sleep stages visited over the course of the night.

However, brain activity that is expressed in the electroencephalogram (EEG) as detected at the scalp, along with eye and muscle movements, gives additional insight into brain mechanisms of sleep, while still remaining non-invasive and safe. Likewise, heart rate and respiration could potentially differentiate between sleep and wake time. For example, simple movement measures from a wrist accelerometer can roughly estimate total sleep time in a multi-day recording. However, human participants can be monitored using a variety of devices to learn more about sleep patterns. Of course, many of the methods for exploring the physiology are highly invasive and only conducted on animals. How sleep achieves this result is not completely understood, but over decades, research has revealed many physiological characteristics of sleep, from molecules to behavior, that have initiated very compelling hypotheses ( Tononi and Cirelli, 2006). Sleep is a requisite aspect of human existence whose most obvious function is to rejuvenate the brain and body on a daily basis. 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. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. 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. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. 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. 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. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. 3Department of Bioengineering, University of California, San Diego, La Jolla, CA, USAīrain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders.2Naval Health Research Center, San Diego, CA, USA.1Institute for Neural Computation, University of California, San Diego, La Jolla, CA, USA.

Hypnogram unit of measurement