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Quantitative Evaluation for the Wakefulness State Using Complexity-Based Decision Threshold Value in EEG Signals

机译:在EEG信号中使用基于复杂性的判定阈值的觉醒状态的定量评估

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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 (P_(θ/α)), and between theta and beta (P_(θ/β)) over occipital lobe. Our index is superior than R.δ, R.θ, P_(θ/α) and P_(θ/β) with 10%, 5.5%, 8.9% and 24.4% respectively.
机译:由于延长的脑电图(脑电图)测量,受试者的完全唤醒状态往往是早期的昏昏欲睡状态。这种情况可以使EEG信号的解释复杂化,因此,应在检查中考虑受试者的助出性。因此,在本研究中,开发了通过使用基于复杂性的判定阈值来定量评估对受试者的觉醒或完全清醒的醒来或完全唤醒的新的索引。所提出的索引基于近似熵(APEN)来量化复杂度度量,而是使用新的系统方法来计算新的参数值。在两组中每一个的眼部闭合期间使用枕骨 - α节奏评估该指数:完全清醒和昏昏欲睡。我们的索引可以显示比用于评估包括相对ΔSub频段功率(R.Δ),相对θ子带功率(R.θ),θ和alpha之间的功率比的其他基于基于谱的基于谱系的优势(P_(θ/α)),以及THETA和β(P_(θ/β))上枕骨叶片。我们的指标优于δ,r.θ,p_(θ/α)和p_(θ/β)分别为10%,5.5%,8.9%和24.4%。

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