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Objective: Visual scoring of 30-s epochs of sleep data is not always adequate to show the dynamic structure of sleep in sufficient details. It is also prone to considerable inter- and intra-rater variability. Moreover, it involves considerable training and experience, and is very tedious, time-consuming, labor-intensive and costly. Hence, automatic sleep staging is needed to overcome these limitations. Since naturally occurring NREM sleep and anesthesia have been reported to possess various underlying neurophysiological similarities, EEG-based depth-of-anesthesia monitors have started to penetrate into sleep research. This study investigates the ability of WAV(CNS) index (as implemented in NeuroSENSE depth-of-anesthesia monitor) to detect NREM sleep stages and wake state for full overnight PSG data.Methods: Full overnight PSG sleep data, obtained from 24 adolescents, was scored by a registered PSG technologist for different sleep stages. Retrospective analysis was performed on a single frontal channel using the WAV(CNS) algorithm. Non-parametric descriptive statistics were used to examine the relationship between WAV(CNS) index and sleep stages.Results: A strong correlation (ρ = 0.9458) was found between the WAV(CNS) index and NREM sleep stages, with WAV(CNS) index values decreasing with increasing sleep stages. Moreover, there was no significant overlap between different NREM sleep stages as classified by the WAV(CNS) index, which was able to significantly differentiate (P < 0.001) between all pairs of Awake and different NREM stages.Conclusions: This study demonstrates that changes in the depth of natural NREM sleep are reflected sensitively by changes in the WAV(CNS) index. Hence, WAV(CNS) index may serve as an automatic real-time indicator of depth of natural sleep with high temporal resolution, and can possibly be of great use for automated sleep staging in routine/postoperative somnographic studies. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this paper is to assess the suitability of brain function monitors for use in closed-loop anesthesia or sedation delivery. In such systems, monitors used as feedback sensors should preferably be Linear and Time Invariant (LTI) in order to limit sensor-induced uncertainty which can cause degraded performance. In this paper, we evaluate the suitability of the BIS A2000 (Aspect Medical Systems, MA), the M-Entropy Monitor (GE HealthCare), and the NeuroSENSE Monitor (NeuroWave Systems Inc, OH), by verifying whether their dynamic behavior conforms to the LTI hypothesis.Methods: We subjected each monitor to two different composite EEG signals containing step-wise changes in cortical activity. The first signal was used to identify Linear Time-Invariant (LTI) models that mathematically capture the dynamic behavior of each monitor. The identification of the model parameters was carried out using standard Recursive Least Squares (RLS) estimation. The second signal was used to assess the performance of the model, by comparing the output of the monitor to the simulated output predicted by the model.Results: While a LTI model was successfully derived for each monitor using the first signal, only the model derived for NeuroSENSE was capable to reliably predict the monitor output for the second input signals. This indicates that some algorithmic processes within the BIS A2000 and M-Entropy are non-linear and/or time variant.Conclusion: While both BIS and M-Entropy monitors have been successfully used in closed-loop systems, we were unable to obtain a unique LTI model that could capture their dynamic behavior during step-wise changes in cortical activity. The uncertainty in their output during rapid changes in cortical activity impose limitations in the ability of the controller to compensate for rapid changes in patients' cortical state, and pose additional difficulties in being able to provide mathematically proof for the stability of the overall closed-loop system. Conversely, the NeuroSENSE dynamic behavior can be fully captured by a linear and time invariant transfer function, which makes it better suited for closed-loop applications. [ABSTRACT FROM AUTHOR]