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

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

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

Detection of Pseudosinusoidal Epileptic Seizure Segments in the Neonatal EEG by Cascading a Rule-Based Algorithm With a Neural Network

Abstract—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 rulebased 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. Index Terms—Electroencephalography, epileptic seizure segment, feedforward neural network (FFNN), neonatal seizure, quantum neural network (QNN). I

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