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The aim of this study was to analyze the magnetoencephalography (MEG) background activity in Attention-Deficit/Hyperactivity Disorder (ADHD) using a regularity measure: sample entropy (SampEn). Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 14 ADHD patients and 14 control subjects. Our results showed that ADHD patients' MEGs were more regular than controls' recordings...
This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied...
The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case...
This manuscript proposes a particle swarm-based feature extraction to monitors brain activity with the goal of identifying correlate cognitive states and intensity of a task. This in turn would allow us to develop a pattern recognition system that will classify such cognitive states and thus to redistribute the workload to other subjects. In this abstract, we present a recognition system that employ...
As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neu-rofibrillary plaques and tangles in the brain that leads to neu-ronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram...
In this paper, we used Recurrence Quantification Analysis (RQA) in order to study pre-epileptic characteristics in rat's EEG recordings. Four adult rats were used to collect epileptic EEG data in an experiment of animal model of epilepsy. Three RQA measures, recurrence rate, determinism, and entropy were calculated from EEG recordings from rats. A moving average filter was used to identify the decreasing...
The goal of this work is to investigate EEG (ElectroEncephaloGram) dynamics after drug intake in patients being in states of Disorders Of Consciousness (DOC) after brain injury. Four patients were involved in the study. All the patients exhibit cerebral lesions located in the same anatomical region. Two nonlinear indexes, such as Lempel-Ziv Complexity (LZC) and Approximate Entropy (ApEn), along with...
Approximate Entropy (ApEn) and Permutation Entropy (PE) have been recently introduced for assessment of anesthetic depth. Both measures have previously been shown to track changes in the electrical brain activity related to the administration of anesthetic agents. In this paper ApEn and PE are compared for the automatic classification of ‘awake’ and ‘anesthetized’ state using a Support Vector Machine...
In this paper, an entropy based method for quantifying the depth of anesthesia from rat EEG is presented. The proposed index for the depth of anesthesia called modified Shannon entropy (MShEn) is based on Shannon entropy (ShEn) and spectral entropy (SpEn) which are widely used for analyzing non-stationary signals. Discrimination power (DP), as a performance indicator for indexes, is defined and used...
This study is aimed at characterizing three signal entropy measures, Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multiscale Entropy (MSE) over real EEG signals when a number of samples are randomly lost due to, for example, wireless data transmission. The experimental EEG database comprises two main signal groups: control EEGs and epileptic EEGs. Results show that both SampEn and ApEn...
This study was undertaken to investigate spectral features derived from EEG signals for measuring cognitive load. Measurements of this kind have important commercial and clinical applications for optimizing the performance of users working under high mental load conditions, or as cognitive tests. Based on EEG recordings for a reading task in which three different levels of cognitive load were induced,...
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