Automatic characterization of sleep need dissipation using a single hidden layer neural network

In the two process sleep model, the rate of sleep need dissipation is proportional to slow wave activity (SWA; EEG power in the 0.5 to 4 Hz band). The dynamics of sleep need dissipation are characterized by two parameters (the initial sleep need S
_o
and the decay rate γ) that can be calculated from SWA values in NREM sleep. The goal in this paper is to use a neural network classifier to automatically detect NREM sleep and estimate Ŝ
_o
and γ̂ using a single EEG signal that is captured during sleep at home. The data from twenty subjects (4 sleep nights per subject) was used in this research. The neural network architecture was optimized using as training and validation sets the EEG sleep data from a previous study. Given the nature of the model, only three stages were considered (NREM, REM, and WAKE). The classification accuracy characterized by the Kappa value achieved in this study dataset was 0.63 (substantial agreement with manual staging) and the specificity/sensitivity for NREM detection were 0.87 and 0.8 respectively. The higher specificity in NREM detection led to systematic S
_o
underestimation (i.e. S
_o
> Ŝ
_o
) and 7 overestimation (i.e. γ <; γ̂). However the variability of the, Ŝo and γ̂ across nights of the same subject is lower compared to the variability of Ŝ
₀
and γ̂ This shows that using automatic staging to characterize sleep need dissipation leads to capturing the most specific and less variable EEG segments that contribute to SWA. This is suitable to characterize sleep need outside sleep lab settings (e.g. at home) that cannot be controlled to the same extent as sleep lab studies.