The potential of biomarkers for diagnosing insomnia: Consensus statement of the WFSBP Task Force on Sleep Disorders.

OBJECTIVES: Thus far, the diagnosis of insomnia is based on purely clinical criteria. Although a broad range of altered physiological parameters has been identified in insomniacs, the evidence to establish their diagnostic usefulness is very limited. Purpose of this WFSBP Task Force consensus paper is to systematically evaluate a series of biomarkers as potential diagnostic tools for insomnia. METHODS: A newly created grading system was used for assessing the validity of various measurements in establishing the diagnosis of insomnia; these measurements originated from relevant studies selected and reviewed by experts. RESULTS: The measurements with the highest diagnostic performance were those derived from psychometric instruments. Biological measurements which emerged as potentially useful diagnostic instruments were polysomnography-derived cyclic alternating pattern, actigraphy, and BDNF levels, followed by heart rate around sleep onset, deficient melatonin rhythm, and certain neuroimaging patterns (mainly for the activity of frontal and pre-frontal cortex, hippocampus and basal ganglia); yet, these findings need replication, as well as establishment of commonly accepted methodology and diagnostic cut-off points. Routine polysomnography, EEG spectral analysis, heart rate variability, skin conductance, thermoregulation, oxygen consumption, HPA axis, and inflammation indices were not shown to be of satisfactory diagnostic value. CONCLUSIONS: Apart from psychometric instruments which are confirmed to be the gold standard in diagnosing insomnia, six biomarkers emerge as being potentially useful for this purpose.

Discrete states of attention during active visual fixation revealed by Markovian analysis of the time series of intrusive saccades.

The frequency of intrusive saccades during maintenance of active visual fixation has been used as a measure of sustained visual attention in studies of healthy subjects as well as of neuropsychiatric patient populations. In this study, the mechanism that generates intrusive saccades during active visual fixation was investigated in a population of young healthy men performing three sustained fixation tasks (fixation to a visual target, fixation to a visual target with visual distracters, and fixation straight ahead in the dark). Markov Chain modeling of inter-saccade intervals (ISIs) was utilized. First- and second-order Markov modeling provided indications for the existence of a non-random pattern in the production of intrusive saccades. Accordingly, the system of intrusive saccade generation may operate in two "attractor" states, one in which intrusive saccades occur at short consecutive ISIs and another in which intrusive saccades occur at long consecutive ISIs. These states might correspond to two distinct states of the attention system, one of low focused - high distractibility and another of high focused - low distractibility, such as those proposed in the adaptive gain theory for the control of attention by the noradrenergic system in the brain. To the authors knowledge, this is the first time that Markov Chain modeling has been applied to the analysis of the ISIs of intrusive saccades.

Sleep EEG and spindle characteristics after combination treatment with clozapine in drug-resistant schizophrenia: a pilot study.

PURPOSE: Clozapine is an atypical neuroleptic agent, effective in treating drug-resistant schizophrenia. The aim of this work was to investigate overall sleep architecture and sleep spindle morphology characteristics, before and after combination treatment with clozapine, in patients with drug-resistant schizophrenia who underwent polysomnography. METHODS: Standard polysomnographic techniques were used. To quantify the sleep spindle morphology, a modeling technique was used that quantifies time-varying patterns in both the spindle envelope and the intraspindle frequency. RESULTS: After combination treatment with clozapine, the patients showed clinical improvement. In addition, their overall sleep architecture and, more importantly, parameters that quantify the time-varying sleep spindle morphology were affected. Specifically, the results showed increased stage 2 sleep, reduced slow-wave sleep, increased rapid eye movement sleep, increased total sleep time, decreased wake time after sleep onset, as well as effects on spindle amplitude and intraspindle frequency parameters. However, the above changes in overall sleep architecture were statistically nonsignificant trends. CONCLUSIONS: The findings concerning statistically significant effects on spindle amplitude and intraspindle frequency parameters may imply changes in cortical sleep EEG generation mechanisms, as well as changes in thalamic pacing mechanisms or in thalamo-cortical network dynamics involved in sleep EEG generation, as a result of combination treatment with clozapine. SIGNIFICANCE: Sleep spindle parameters may serve as metrics for the eventual development of effective EEG biomarkers to investigate treatment effects and pathophysiological mechanisms in schizophrenia.

Amplitude normalization applied to an artificial neural network-based automatic sleep spindle detection system.

Sleep spindles are significant rhythmic transients present in the sleep electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Automatic sleep spindle detection techniques are sought for the automation of sleep staging and the detailed study of sleep spindle patterns, of possible physiological significance. A deficiency of many of the available automatic detection techniques is their reliance on the amplitude level of the recorded EEG voltage values. In the present work, an automatic sleep spindle detection system that has been previously proposed, using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), was evaluated using a voltage amplitude normalization procedure, with the aim of making the performance of the ANN independent of the absolute voltage level of the individual subjects' recordings. The application of the normalization procedure led to a reduction in the false positive rate (FPR) as well as in the sensitivity. When the ANN was trained on a combination of data from healthy subjects, the reduction of FPR was from 42.6% to 19%, while the sensitivity of the ANN was kept at acceptable levels, i.e., 73.4% for the normalized procedure vs 84.6% for the non-normalized procedure.

Differences in EEG delta frequency characteristics and patterns in slow-wave sleep between dementia patients and controls: a pilot study.

PURPOSE: To evaluate the modifications of EEG activity during slow-wave sleep in patients with dementia compared with healthy elderly subjects, using spectral analysis and period-amplitude analysis. METHODS: Five patients with dementia and 5 elderly control subjects underwent night polysomnographic recordings. For each of the first three nonrapid eye movement-rapid eye movement sleep cycles, a well-defined slow-wave sleep portion was chosen. The delta frequency band (0.4-3.6 Hz) in these portions was analyzed with both spectral analysis and period-amplitude analysis. RESULTS: Spectral analysis showed an increase in the delta band power in the dementia group, with a decrease across the night observed only in the control group. For the dementia group, period-amplitude analysis showed a decrease in well-defined delta waves of frequency lower than 1.6 Hz and an increase in such waves of frequency higher than 2 Hz, in incidence and amplitude. CONCLUSIONS: Our study showed (1) a loss of the dynamics of delta band power across the night sleep, in dementia, and (2) a different distribution of delta waves during slow-wave sleep in dementia compared with control subjects. This kind of computer-based analysis can highlight the presence of a pathologic delta activity during slow-wave sleep in dementia and may support the hypothesis of a dynamic interaction between sleep alteration and cognitive decline.

Performance evaluation of an Artificial Neural Network automatic spindle detection system.

Sleep spindles are transient waveforms found in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Sleep spindles are used for the classification of sleep stages and have been studied in the context of various psychiatric and neurological disorders, such as Alzheimer's disease (AD) and the so-called Mild Cognitive Impairment (MCI), which is considered to be a transitional stage between normal aging and dementia. The visual processing of whole-night sleep EEG recordings is tedious. Therefore, various techniques have been proposed for automatically detecting sleep spindles. In the present work an automatic sleep spindle detection system, that has been previously proposed, using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), is evaluated in detecting spindles of both healthy controls, as well as MCI and AD patients. An investigation is carried also concerning the visual detection process, taking into consideration the feedback information provided by the automatic detection system. Results indicate that the sensitivity of the detector was 81.4%, 62.2%, and 83.3% and the false positive rate was 34%, 11.5%, and 33.3%, for the control, MCI, and AD groups, respectively. The visual detection process had a sensitivity rate ranging from 46.5% to 60% and a false positive rate ranging from 4.8% to 19.2%.

Single-trial magnetoencephalography signals encoded as an unfolding decision process.

The model of a stochastic decision process unfolding in motor and premotor regions of the brain was encoded in single-trial magnetoencephalographic (MEG) recordings while ten healthy subjects performed a sensorimotor Reaction Time (RT) task. The duration of single-trial MEG signals preceding the motor response, recorded over the motor cortex contralateral to the responding hand, co-varied with RT across trials according to the model's prediction. Furthermore, these signals displayed the same properties of a "rising-to-a-fixed-threshold" decision process as posited by the model and observed in the activity of single neurons in the primate cortex. The present findings demonstrate that non-averaged, single-trial MEG recordings can be used to test models of cognitive processes, like decision-making, in humans.

Independent component analysis for source localization of EEG sleep spindle components.

Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11-16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle "components" (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.

A multistage system for the automated detection of epileptic seizures in neonatal electroencephalography.

This paper describes the design and test results of a three-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage and identifies overlapping 5-second segments of suspected seizure activity in each EEG channel. In stage II, the detected segments from stage I are spatiotemporally clustered to produce multichannel candidate seizures. In stage III, the candidate seizures are processed further using measures of quality and context-based rules to eliminate false candidates. False candidates because of artifacts and commonly occurring EEG background patterns such as bifrontal delta activity are also rejected. Seizures at least 10 seconds in duration are considered for reporting results. The testing data consisted of recordings of 28 seizure subjects (34 hours of data) and 48 nonseizure subjects (87 hours of data) obtained in the neonatal intensive care unit. The data were not edited to remove artifacts and were identical in every way to data normally processed visually. The system was able to detect seizures of widely varying morphology with an average detection sensitivity of almost 80% and a subject sensitivity of 96%, in comparison with a team of clinical neurophysiologists who had scored the same recordings. The average false detection rate obtained in nonseizure subjects was 0.74 per hour.

Independent components of sleep spindles.

Sleep spindles are considered a hallmark of stage 2 of the sleep electroencephalogram (EEG) and are used both for sleep staging and for clinical studies of pharmacological agents. Analyses of sleep spindle topography, as well as intracranial source investigations provided evidence for the existence of two distinct sleep spindle types, "slow" and "fast" spindles at approximately 12 and 14 Hz, respectively. The aim of the present study was to apply Independent Component Analysis (ICA) to sleep spindles, for examining the possibility of extracting, through visual analysis of the spindle EEG and selection of Independent Components (ICs), spindle "components" corresponding to separate EEG activity patterns, and to investigate the sources underlying these spindle components. The inverse electromagnetic problem was solved using Low-Resolution Brain Electromagnetic Tomography (LORETA). Results indicate separability and stability of sources related to sleep spindle components reconstructed from separate groups of ICs.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study.

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.