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A Aarabi,
R Grebe,
P Berquin,
E Bourel Ponchel,
C Jalin,
M Fohlen,
C Bulteau,
O Delalande,
C Gondry,
C Héberlé,
V Moullart,
F Wallois
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ABSTRACT: This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states.
High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT.
The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states.
High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the spatiotemporal characteristics of sources located in the epileptic focus. The results were validated by standard methods, ensuring good spatial resolution by MRI and SPECT and optimal temporal resolution by intracerebral EEG. Surface EEG can be used to identify different spike clusters and sources of the successive epileptic states. The method that was used in this study will provide physicians with a better understanding of the pathophysiological characteristics of epileptic activities. In particular, this method may be useful for more effective positioning of implantable intracerebral electrodes.
Neurophysiologie Clinique/Clinical Neurophysiology 06/2012; 42(4):207-24. · 1.98 Impact Factor
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ABSTRACT: In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: Recent studies described several changes of attention-related components of late frontal event-related potentials (ERPs) during Go/NoGo paradigm in children with attention-deficit/hyperactivity disorder (ADHD). We aimed to determine whether ERP components corresponding to earlier encoding of visual incoming information are also modulated by attentional disorders.
We recorded high-resolution EEG in 15 children meeting DSM-IV criteria for ADHD, comprising 15 age-matched control groups during an equiprobable Go/NoGo task in a cued continuous performance test (CPT-AX) paradigm. Both P100 and N200 ERP components were measured in response to both Go and NoGo stimuli. We analyzed both components with SwLORETA in order to localize their brain sources.
A low rate of Go correct response and high rate of omission errors were observed in ADHD children. When compared to controls, these displayed delayed P100 and N200 latency, and lower P100-NoGo amplitude. In addition, the P100 latency was delayed for NoGo compared to Go condition. The source of P100 was located in occipital area. A sizable decrease in early electrical activity was found in ADHD, especially in the NoGo condition.
Our results suggest an early deficit in visual sensory integration within the occipital cortex in children with ADHD.
Neurophysiologie Clinique/Clinical Neurophysiology 06/2010; 40(3):137-49. · 1.98 Impact Factor
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ABSTRACT: The mechanisms that drive neurons to synchronize in epileptic spikes are still subject to debate. In the present study, we used a combination of electrocorticography and near-infrared spectroscopy (ECoG/NIRS) to evaluate haemodynamic changes before, during and after epileptic spikes induced by administration of bicuculline methiodide (BM) onto the sensorimotor cortex in 8 adult Sprague-Dawley rats. Simultaneous ECoG/NIRS signals were recorded during an initial reference period (to measure spontaneous bioelectrical/metabolic activities) and then again 60 min after BM administration. Spikes in the ECoG were detected by an in-house program based on MatLab 7.0. The appearance times of the P1 peaks were used to determine corresponding time periods in the NIRS for further analysis. We observed a pronounced pre-spike modification in the haemodynamics, which became visible latest 5 s before the spike, achieving after some oscillations its minimum at round about the P1 appearance time. The post-spike period was characterized by an initial increase in oxyhaemoglobin (HbO) and total haemoglobin (HbT) to a maximum at about 2 s after the spike followed by a phase of declining oscillations disappearing after 10 to 15 s after the spike. We discuss the mechanisms underlying the haemodynamic and electrical changes that occur before, during and after epileptiform spikes. The haemodynamic changes observed with NIRS and occurring before the spikes constitute a haemodynamic predictor of electrical synchronization of spikes.
NeuroImage 04/2010; 50(2):600-7. · 5.89 Impact Factor
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ABSTRACT: In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate.
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009
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ABSTRACT: We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy.
We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system.
The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis.
The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns.
This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 08/2009; 120(9):1648-57. · 3.12 Impact Factor
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ABSTRACT: In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:1860-3.
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ABSTRACT: Different types of analyses of scalp and intracranial electroencephalography (EEG) recordings using linear and nonlinear time series analysis method have been done. They showed strong evidence of detectable changes in the EEG dynamics from minutes up to several hours in advance of seizure onset. The predictive performance of univariate and bivariate measures, comprising both linear and non-linear approaches have been carried in different studies Direct comparison among different measures and methods in seizure prediction is not possible, unless they are applied to the same dataset. In this review paper, we describe different seizure prediction measures briefly and discuss the existing challenges.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:1864-7.
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ABSTRACT: Automatic seizure detection obtains valuable information concerning duration and timing of seizures. Commonly used methods for EEG seizure detection in adults are inadequate for the same task in neonates because they lack the specific age-dependant characteristics of normal and pathological EEG. This paper presents an automatic seizure detection system for newborn with focus on feature selection via relevance and redundancy analysis.
Two linear correlation-based feature selection methods and the ReliefF method were applied to parameterized EEG data acquired from six neonates aged between 39 and 42 weeks. To evaluate the effectiveness of these methods, features extracted from seizure and non-seizure segments were ranked by these methods. The optimized ranked feature subsets were fed into a backpropagation neural network for classifying. Its performance was used as indicator for the feature selection effectiveness.
Results showed an average seizure detection rate of 91%, an average non-seizure detection rate of 95%, an average false rejection rate of 95% and an overall average detection rate of 93% with a false seizure detection rate of 1.17/h.
This good performance in detecting newborn ictal activities has been achieved based on an optimized subset of 30 features determined by the ReliefF-based detector, which corresponds to a reduction of the number of features of up to 75%.
The presented approach takes into account specific characteristics of normal and pathological EEG. Thus, it can improve the accuracy of conventional seizure detection systems in newborn.
Clinical Neurophysiology 03/2006; 117(2):328-40. · 3.41 Impact Factor
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ABSTRACT: The role of feature selection is fundamental in pattern recognition, increasing accuracy and lowering complexity and computational cost in the presence of redundant and irrelevant features. This paper outlines a new feature selection algorithm based on discriminant and redundancy analysis to determine the "goodness" of feature subsets. The performance of this method was compared to a correlation-based feature selection method via relevance and redundancy analysis. To evaluate their effectiveness for seizure detection in newborn, the features extracted from seizure and non-seizure segments were ranked by these methods. Then, the optimized ranked feature subsets were fed to multilayer backpropagation neural networks as the classifiers. The classifier performance was used as indicator of the feature selection effectiveness. The results showed an average seizure detection rate of 90%, an average non-seizure detection rate of 91%, an average false rejection rate of 91% and an average detection rate of 90%. Our feature selection method allows a feature reduction up to 80%
Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on; 04/2005
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Clinical Neurophysiology. 118(12):2781-2797.
