Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity - PubMed (original) (raw)

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Julie A Onton et al. Front Hum Neurosci. 2016.

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.

Keywords: EEG; deep sleep; electrodermal activity; sleep; sleep scoring algorithm; slow wave sleep; spectral decomposition.

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Figures

Figure 1

Figure 1

Typical sleep report showing whole-night sleep electroencephalogram (EEG) from a single forehead electrode (referenced to mastoid). This night shows Hi Deep sleep in the first and second cycles and Lo Deep sleep in the third and fourth cycles (with a final Lo Deep period before 7 h). Brief power in the high frequency range is indicated with cyan vertical lines—moments of likely micro-arousals when electrodes were moved or high frequency brain activity was temporarily active, or both.

Figure 2

Figure 2

Total sleep time for this population, shown in (A) varied from 4.2 h to 10.7 h (mean = 6.8 h). (B) Shows the sleep onset latency, which varied between 8.6 min and 214.3 min with a median of 18.9 min. The vertical dotted lines in each plot indicate the mean or median of each distribution.

Figure 3

Figure 3

(A) Demonstrates how electrodermal activity (EDA; orange trace, second plot down) tends to peak during Lo Deep sleep, usually near the end of the stage. EDA magnitude can vary across cycles. (B) Shows another example of EDA increasing during Lo Deep but not during Hi Deep sleep. In this case, EDA peaked before the end of Lo Deep but still showed an accelerated decline at the offset of Lo Deep sleep.

Figure 4

Figure 4

(A) The highest mean EDA was usually recorded in a Lo Deep stage of sleep. (B) The highest mean EDA was most commonly recorded in the second quarter of the night, although the highest mean EDA could occur during any quadrant of the night. (C) EDA tends to be higher during the last minute of Lo Deep stages compared with the first minute of the stage, meaning that EDA tends to rise during Lo Deep (error bars show the standard error of the mean). (D) Percentage of time in each stage that EDA was rising rather than falling (using the derivative of EDA measurement). This pattern again shows that EDA tends to increase most dramatically during Lo Deep sleep.

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