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

Automated Sleep Spindle Detection System using Period-Amplitude Analysis

Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations

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 non-significant 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

Sleep in Frontotemporal Dementia is Equally or Possibly More Disrupted, and at an Earlier Stage, When Compared to Sleep in Alzheimer's Disease

Background: Conversely to other neurodegenerative diseases (i.e., Alzheimer’s disease, AD), sleep in frontotemporal dementia (FTD) has not been studied adequately. Although some evidence exists that sleep-wake disturbances occur in FTD, very little is known regarding sleep macrostructure and/or primary sleep disorders. Objective: To investigate these issues in this population and compare them to similar issues in AD and in healthy elderly (HE). Methods: Twelve drug-naive behavioral-variant FTD (bvFTD) patients (7 men/5 women) of mean age 62.5 +/- 8.6 years were compared to seventeen drug-naive AD patients (8 men/9 women) of mean age 69.0 +/- 9.9 years and twenty drug-naive HE (12 men/8 women) of mean age 70.2 +/- 12.5 years. All participants were fully assessed clinically, through a sleep questionnaire, an interview, and video-polysomnography recordings. Results: The two patient groups were comparably cognitively impaired. However, compared to FTD patients, the AD patients had a statistically significant longer disease duration. Overall, the sleep profile was better preserved in HE. Sleep complaints did not differ considerably between the two patient groups. Sleep parameters and sleep macrostructure were better preserved in AD compared to FTD patients, regardless of primary sleep disorders, which occurred equally in the two groups. Conclusions: With respect to AD, FTD patients had several sleep parameters similarly or even more affected by neurodegeneration, but in a much shorter time span. The findings probably indicate a centrally originating sleep deregulation. Since in FTD patients sleep disturbances may be obvious from an early stage of their disease, and possibly earlier than in AD patients, physicians and caregivers should be alert for the early detection and treatment of these symptoms

Non-rapid eye movement sleep instability in mild cognitive impairment: a pilot study

Objective: Polysomnographic (PSG) studies in mild cognitive impairment (MCI) are not conclusive and are limited only to conventional sleep parameters. The aim of our study was to evaluate sleep architecture and cyclic alternating pattern (CAP) parameters in subjects with MCI, and to assess their eventual correlation with cognition. Methods: Eleven subjects with MCI (mean age 68.5 +/- 7.0 years), 11 patients with mild probable Alzheimer's disease (AD; mean age 72.7 +/- 5.9 years), referred to the Outpatient Cognitive Disorders Clinic, and 11 cognitively intact healthy elderly individuals (mean age 69.2 +/- 12.6 years) underwent ambulatory PSG for the evaluation of nocturnal sleep architecture and CAP parameters. Results: Rapid eye movement sleep, CAP rate, and CAP slow components (A1 index) were decreased in MCI subjects and to a greater extent in AD patients, compared to cognitively intact controls. AD showed also decreased slow wave sleep (SWS) relative to healthy elderly individuals. MCI nappers showed decreased nocturnal SWS and A1 subtypes compared to non-nappers. Several correlations between sleep variables and neuropsychological tests were found. Conclusions: MCI and AD subjects showed a decreased sleep instability correlated with their cognitive decline. Such a decrease may be considered as a potential biomarker of underlying neurodegeneration. (C) 2015 Elsevier B.V. All rights reserved

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

International audienceAppropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain