Quantitative evaluation for the wakefulness state using complexity-based decision threshold value in EEG signals.

Fully awake state of the subjects tends to be an early drowsy state as a result from the prolonged time of electroencephalography (EEG) measurements. Such situations can complicate the interpretation of EEG signals and hence, the wakefulness of the subject should be considered in the inspection. Thus, in the present study, a new index for quantitative evaluation of the wakefulness (whether either early drowsy or fully awake) state of subjects by using a complexity-based decision threshold value was developed. The proposed index was based on approximate entropy (ApEn) to quantify the complexity metric, but with new parameter values by using a new systematic approach. This index was evaluated using occipital-alpha rhythm during eye closure for 45 healthy adult subjects for each one of two groups: fully awake and drowsy groups. Our index could show more superiority than other conventional spectral-based indices used for evaluating the wakefulness state of subjects including relative delta sub band power (R.δ), relative theta sub band power (R.θ), power ratio between theta and alpha (Pθ/α), and between theta and beta (Pθ/β) over occipital lobe. Our index is superior than R.δ, R.θ, Pθ/α and Pθ/β with 10%, 5.5%, 8.9% and 24.4% respectively.