Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness.

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

Detection and prediction of driver drowsiness using artificial neural network models.

Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted.

The relation of cognitive load and pupillary unrest.

OBJECTIVE: This study examines the relationship between pupillary unrest (PU) and cognitive load. BACKGROUND: PU represents a measure of reduced central nervous tonic arousal (sleepiness). A loss of tonic arousal can be associated with difficulties in maintaining the required level of performance. Thus, the measurement of tonic arousal in occupational contexts can help to prevent overload or errors. METHOD: We compared a group with high cognitive load (30 apron controllers of an international airport) with a control group with low cognitive load (63 healthy people during their free time) in a non-randomized experimental design with pre- and post-test assessment of PU. PU was scanned by an infrared sensor with a frequency of 25 Hz and a resolution of .05 mm. To control for circadian effects, measurements for both groups were taken at the same time of the day. RESULTS: High PU at the start of the shift correlated with high perceived load during the shift. There were no indications of reverse effects. Analyses of group x time effects with generalized linear models (repeated measures) revealed that cognitive load did in no way affected PU. CONCLUSION: Initially low tonic arousal (indicated by high PU) may predict subsequent workload, but being exposed to high cognitive load does not influence tonic arousal after the end of the shift. With that, the study contributes to valid interpretations of pupillary unrest measurements in occupational contexts.

Algorithms for sleep-wake identification using actigraphy: a comparative study and new results.

The aim of this study was to investigate two new scoring algorithms employing artificial neural networks and decision trees for distinguishing sleep and wake states in infants using actigraphy and to validate and compare the performance of the proposed algorithms with known actigraphy scoring algorithms. The study employed previously recorded longitudinal physiological infant data set from the Collaborative Home Infant Monitoring Evaluation (CHIME) study conducted between 1994 and 1998 [http://dccwww.bumc.bu.edu/ChimeNisp/Main_Chime.asp; Sleep26 (1997) 553] at five clinical sites around the USA. The original CHIME data set contains recordings of 1079 infants <1 year old. In our study, we used the overnight polysomnography scored data and ankle actimeter (Alice 3) raw data for 354 infants from this data set. The participants were heterogeneous and grouped into four categories: healthy term, preterm, siblings of SIDS and infants with apparent life-threatening events (apnea of infancy). The selection of the most discriminant actigraphy features was carried out using Fisher's discriminant analysis. Approximately 80% of all the epochs were used to train the artificial neural network and decision tree models. The models were then validated on the remaining 20% of the epochs. The use of artificial neural networks and decision trees was able to capture potentially nonlinear classification characteristics, when compared to the previously reported linear combination methods and hence showed improved performance. The quality of sleep-wake scoring was further improved by including more wake epochs in the training phase and by employing rescoring rules to remove artifacts. The large size of the database (approximately 337,000 epochs for 354 patients) provided a solid basis for determining the efficacy of actigraphy in sleep scoring. The study also suggested that artificial neural networks and decision trees could be much more routinely utilized in the context of clinical sleep search.

[Videoelectroencephalography in children with paroxysmal events]. / Wideoelektroencefalografia w stanach napadowych u dzieci.

The paper presents the crucial role of video EEG, modern diagnostic method, which allowed synchronized recording of clinical status and EEG pattern of the patient. This method gives the possibility to compare these two parameters in term of paroxysmal events. Video EEG allows to diagnosed clinical events associated with bioelectrical discharges (epilepsy), recording of bioelectrical events without clinical seizures, diagnosing clinical attacks without bioelectrical discharges (pseudoseizures) and nonepileptic events (without epileptic character in video and EEG). This method is very useful especially in children and adolescents because of huge polymorphism of clinical signs, more common ambiguous diagnosis in this age and due to heterogeneity of bioelectrical brain function in children. Video EEG monitoring gives the possibility for clinical and electro-physiological interpretation of paroxysmal events and plays a crucial rule in localizing of epileptogenic focus, classification of the seizure, epilepsy type or syndrome. The role of suggestion and placebo is important in diagnosing psychogenic pseudoseizures. The duration of video EEG recording is differentiated and much more shorter in diagnosing the type of the event. Prolonged monitoring is needed in children with drag resistant epilepsy and in pre-operation evaluation.

Daytime parahypnagogia: a state of consciousness that occurs when we almost fall asleep.

Based on a series of self-reports of a previously undescribed and undocumented experiential event, we are postulating the existence of a newly identified state of consciousness, daytime parahypnagogia (DPH). DPH is more likely to occur when one is tired, bored, suffering from attention fatigue, and/or engaged in a passive activity. Individuals describe DPH as a transient and fleeting episode that is dissociative, trance-like, dreamlike, uncanny, and often pleasurable; but, unlike a daydream, it is not self-directed. A DPH episode is spontaneous and may consist of a flash image, thought, and/or creative insight that is quickly forgotten. However, the individual remains aware of having had a DPH experience. This paper details the experiential characteristics associated with DPH. Through a brief review of the literature, the authors differentiate DPH from related phenomena and establish DPH as a unique and distinct altered state of consciousness.

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

Physiological classification of sleep-wake states: based on tri-vesicular (3V) model of the brain.

A new physiological classification of sleep-wake states, based on a novel Tri-Vesicular (3V) model of the brain is proposed. The 3V model consists of an interconnected network of three primal brain vesicles, namely, right and left Arch-Encephalon (Mesencephalon + Diencephalon + Telencephalon) and one DeuterEncephalon (Metencephalon + Myelencephalon). Nine sleep-wake states are defined on the basis of the central activational index (activation and/or inhibition of the 3 brain vesicles), and the level of global arousal. Four sleep states I-IV, four wake states I-IV, and one transitional sleep-wake state, are characterized. The four sleep states correspond with the four non-REM sleep stages, the transitional sleep-wake state correlates with REM sleep, and four wake states are defined in terms of minimal, low, moderate, and high, global behavioral arousal. Three sets of data in the form of polysomnographic and aerobic exercise studies in five adult subjects, and 30 days' data of self-monitored arousal and oro-nasal breathing patterns, are provided in support of this physiological classification of sleep-wake states and the 3V brain model.