Evaluation of an automated single-channel sleep staging algorithm

Abstract

BackgroundWe previously published the performance evaluation of an automated electroencephalography (EEG)-based single-channel sleep–wake detection algorithm called Z-ALG used by the Zmachine® sleep monitoring system. The objective of this paper is to evaluate the performance of a new algorithm called Z-PLUS, which further differentiates sleep as detected by Z-ALG into Light Sleep, Deep Sleep, and Rapid Eye Movement (REM) Sleep, against laboratory polysomnography (PSG) using a consensus of expert visual scorers.MethodsSingle night, in-lab PSG recordings from 99 subjects (52F/47M, 18–60 years, median age 32.7 years), including both normal sleepers and those reporting a variety of sleep complaints consistent with chronic insomnia, sleep apnea, and restless leg syndrome, as well as those taking selective serotonin reuptake inhibitor/serotonin–norepinephrine reuptake inhibitor antidepressant medications, previously evaluated using Z-ALG were re-examined using Z-PLUS. EEG data collected from electrodes placed at the differential-mastoids (A1–A2) were processed by Z-ALG to determine wake and sleep, then those epochs detected as sleep were further processed by Z-PLUS to differentiate into Light Sleep, Deep Sleep, and REM. EEG data were visually scored by multiple certified polysomnographic technologists according to the Rechtschaffen and Kales criterion, and then combined using a majority-voting rule to create a PSG Consensus score file for each of the 99 subjects. Z-PLUS output was compared to the PSG Consensus score files for both epoch-by-epoch (eg, sensitivity, specificity, and kappa) and sleep stage-related statistics (eg, Latency to Deep Sleep, Latency to REM, Total Deep Sleep, and Total REM).ResultsSensitivities of Z-PLUS compared to the PSG Consensus were 0.84 for Light Sleep, 0.74 for Deep Sleep, and 0.72 for REM. Similarly, positive predictive values were 0.85 for Light Sleep, 0.78 for Deep Sleep, and 0.73 for REM. Overall, kappa agreement of 0.72 is indicative of substantial agreement.ConclusionThis study demonstrates that Z-PLUS can automatically assess sleep stage using a single A1–A2 EEG channel when compared to the sleep stage scoring by a consensus of polysomnographic technologists. Our findings suggest that Z-PLUS may be used in conjunction with Z-ALG for single-channel EEG-based sleep staging.

Case comparison of sleep features from ear-EEG and scalp-EEG

“…Single-channel A1-A2 EEG and automated sleep staging algorithms have recently demonstrated high agreement rate with PSG [6] . Abbreviated montages for sleep staging have also been successful [7] and it is thus conceivable that other single-channel locations could also perform well in algorithmic sleep staging.…”

Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders

“…In addition, the simpler mechanism of EEG is preferred for the recording of naturalistic sleep, since untrained subjects will be required to use this method in their home. Accordingly, single channel EEG has recently attracted attention ( 19 – 21 ), although its reliability has not been compared to that of activity-based sleep recorders. To address this question, an EEG recorder Sleep Scope (Sleepwell, Osaka, Japan) was used in the present study, whose prototype device has been previously validated in patients with sleep disorders ( 22 ).…”

Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study

“…Sleep EEG was collected using a Zmachine Insight+ device (General Sleep Corporation). The Zmachine Insight+ is a single-channel EEG device with accompanying software that provides sleep staging scoring [ 25 , 26 ]. Participants were instructed to place one disposable (ie, one-time use) sensor behind each ear and one on the center of their neck directly prior to getting into bed each evening, and remove the sensors immediately following their final awakening.…”

“…This is possible because they obtain EEG data exclusively from outside the hairline (typically from the forehead and sometimes the mastoids), whereas obtaining scalp EEG data from the usual locations employed for in-laboratory studies involves placing electrodes within the hairline, which requires a PSG technologist involvement in order to obtain adequate recordings. Some provide automatic scoring of sleep behaviors and architecture that are user-friendly for patients and providers [ 25 , 26 ]. Among adults, consumer-grade sleep EEG wearables have been used in research and clinic settings [ 27 , 28 ].…”