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Query: UMLS:C0036572 (seizures)
 80,221 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Estimation of autospectra and coherence and phase spectra of seizure EEG, using the FFT technique, will cause "smearing" of the rapid dynamic changes which occur during the seizure. This is inherent to FFT spectral estimation, due to the averaging process which is necessary in order to get consistent spectral estimates. A different approach suggested in the present study is to carry out multivariate autoregressive modeling of the multichannel seizure EEG, combined with adaptive segmentation. In order to obtain good estimates in cases of short record length, the vectorial AR modeling was based on residual energy ratios. The method has been tested on multichannel seizure EEG recordings from rats with focal epilepsy, caused by intracerebral administration of Kainic acid, and in depth EEG recordings in patients with temporal lobe epilepsy.
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PMID:On the tracking of rapid dynamic changes in seizure EEG. 147 24

The present study deals with comparative evaluation of various methods for time delay estimations applied to multichannel seizure EEG. The different methods included block algorithms, both in time and frequency domains (such as General Crosscorrelation, FFT, AR), and a new method for time delay estimation based on adaptive least-squares filtering. The various time delay estimators were tested on simulated signals and on real multichannel EEG recorded from rats having generalized seizures with focal onset. The adaptive least-squares filtering (the lattice-ladder type) has been found as the most efficient for time delay estimation.
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PMID:On time delay estimation of epileptic EEG. 795 9

The aim of this study was to investigate whether EEG source localization in the frequency domain, using the FFT dipole approximation (Lehmann, D. and Michel, C.M. Electroenceph. clin. Neurophysiol., 1990, 76: 271-276), would be useful for quantifying the frequency content of epileptic seizure activity. Between one and 7 extracranially recorded seizures were analyzed in each of 7 patients with mesolimbic epilepsy, who were seizure-free after temporal lobe resection. The full scalp frequency spectrum for the first 4 s after seizure onset, as well as for subsequent periods, was determined. Power peaks in the spectra were identified, and an instant dipole fit was performed for the frequencies corresponding to these peaks. Ictal frequencies, ranging between 3.5 and 8.5 Hz, showed a variable degree of stability over time in the different patients. For a particular frequency, dipole results were similar during the different phases of seizure development. In patients with more than one prominent frequency, dipole results for the different frequencies were similar. Dipole results were also similar between patients. We conclude that dipole localization of dominant frequencies, as obtained from full scalp FFT analysis, gives quite reproducible results for seizures originating in the mesial temporal area. The method may become a useful tool for the pre-surgical identification of patients with mesolimbic epilepsy.
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PMID:Frequency domain EEG source localization of ictal epileptiform activity in patients with partial complex epilepsy of temporal lobe origin. 1034 37

Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.
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PMID:Non-stationary distributed source approximation: an alternative to improve localization procedures. 1150 Sep 92

Tramadol, an analgesic with mean potency one tenth that of morphine is used regularly for the treatment of chronic and postoperative pain. Previous reports have indicated that tramadol may induce seizure activity when given together with a selective serotonin reuptake inhibitor (SSRI). Therefore, its major mode of action may be questioned which purportedly is due to binding with the opioid receptor and partly due to the inhibition of monoamine reuptake. We therefore set out to study its potential in inducing seizure activity and to quantify its effect on EEG-power spectra and on the central modulation of sensory afferents in awake and trained dogs (n=7). In order to demonstrate if opioid receptors mediated these effects, incremental doses of tramadol were given which was followed by naloxone for possible reversal. After a wash-out period the same animals were exposed to graded doses of alfentanil, a pure mu-receptor agonist. Again this was followed by the opioid antagonist naloxone for reversal.The electroencephalogram (EEG) and the event-related evoked potentials (SEP) were used to demonstrate possible excitatory effects. In order to derive the SEP the front paw was stimulated electrically (Digi Stim II trade mark ) while the evoked potentials were picked up contralaterally from the somatosensory cortex using stick-on electrodes. 256 sweeps were averaged (Lifescan) and the peak-to-peak amplitude was measured to demonstrate CNS excitation compared to control (%). Additionally, the raw electroencephalogram was viewed for epileptogenic changes and its power computed into the various power bands alpha, beta, delta und theta using FFT over a time epoch of 60 s. Following control, graded doses of either tramadol (2-5-10 mg/kg i.v.) or alfentanil (10-30-60 microg/kg i.v.) were given every 15 min while the EEG and the SEP were recorded. Thereafter naloxone (20 microg/kg i.v.) was injected for reversal. Tramadol did not suppress the amplitude of the SEP at any dose. High doses (>5 mg/kg i.v.) resulted in an increase (+100%) of the amplitude of the evoked potential. This was accompanied by short-term muscle fibrillations, and a short-term spike-and-wave activity in the EEG followed by a long-lasting theta-dominance. These effects could not be reversed by naloxone. In contrast to tramadol, alfentanil induced a dose-related depression of amplitude in the SEP with a maximum of 82% suggesting a depressive effect of modulation of afferents in the sensory cortex. This effect was fully naloxone reversible and was followed by a rebound in amplitude of the SEP together with an increase in fast beta-waves in the EEG. Tramadol very little mediates its central action via the mu-opioid receptor as the present effects were not naloxone reversible. Consistent with the results is the very low affinity of tramadol to the opioid receptor which is several thousand times less than that of morphine. Most likely, inhibition of central norepinephrine and serotonin reuptake as well as the reduction in 5-HT-turnover may contribute to the effects of tramadol. Due to the monoamine reuptake inhibition an increase in transmission may result, triggering off excitatory phenomena with spike-and-wave activity in the CNS. Such excitatory effects, however, may only be seen when tramadol is used in doses exceeding the therapeutic range.
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PMID:[The opioid tramadol demonstrates excitatory properties of non-opioid character--a preclinical study using alfentanil as a comparison]. 1279 88

Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with WNN, we obtained novel and reliable classifier architecture. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN and logistic regression based counterpart. Within the same group, the WNN-based classifier was more accurate than the FEBANN-based classifier, and the logistic regression-based classifier.
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PMID:Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. 1592 85

While the peri-infarct cortex is thought to be responsible for functional recovery, the site is also a strong candidate for post-stroke seizures. Since it is crucial to identify the conditions when the site is changed with such beneficial or detrimental results, the peri-infarct changes were investigated before and just after inducing a focal infarct on rat cortex. The receptive fields in the peri-infarct cortex began to increase a few hours after the infarct, and reached a statistical significance at 6 hours (Dunnett post hoc tests; p<0.05). In temporal association with these changes, EEG in the peri-infarct cortex showed epileptiform activities containing large-amplitude spike-and-wave discharges. The gross amplitude, peak-to-peak amplitude and burst frequency showed statistically significant increases within 4 hours, in comparison to those of the controls (Dunnett post hoc tests; p<0.05). FFT power spectrum analyses showed a distinct increase in approximately 25 Hz frequency bands in the post-stroke groups. The homogeneous area of the contralateral hemisphere in the infarct group, in contrast, did not show such plastic or excitability changes. This study demonstrated, for the first time, that the peri-infarct cortex acquires the characteristics of potential epileptogenesis and functional recovery within hours of a stroke.
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PMID:Epileptiform discharges and neuronal plasticity in the acute peri-infarct cortex of rats. 1968 9

Epilepsy is a common neurological disorder that is characterized by recurrent unprovoked seizures. Epilepsy can develop in any person at any age. 0.5% to 2% of people will develop epilepsy during their lifetime. This paper aims to develop the clinical decision support system (DSS) for the diagnosis of epilepsy. In this paper a simple, reliable and economical Neural Network (NN) based DSS was proposed for the diagnosis of epilepsy. The generalized feed forward neural network (GFFNN) was designed for the diagnosis. Eleven statistical parameters along with the 64 FFT were extracted for the electroencephalogram (EEG) signal. Data used for the experimentation purpose was obtained from the University of Bonn. The classification rate of GFFNN was 100 % for the training data and 86.67% for the cross validation data.
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PMID:Epilepsy diagnosis based on generalized feed forward neural network. 2329 94

Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a total of 44 features including 8 absolute spectral powers, 8 relative spectral powers and 28 spectral power ratios are extracted every two seconds using a 4-second window with a 50% overlap. These features are then ranked and selected in a patient-specific manner using a two-step feature selection. Selected features are further processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains 80 seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains 78 seizures from 17 children in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100% and an average false positive rate (FPR) of 0.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68% and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is always left out from the training set. The proposed algorithm also has a low complexity as the spectral powers can be computed using FFT. The area and power consumption of the proposed linear SVM are 2 to 3 orders of magnitude less than a radial basis function kernel SVM (RBF-SVM) classifier. Furthermore, the total energy consumption of a system using linear SVM is reduced by 8% to 23% compared to system using RBF-SVM.
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PMID:Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power. 2652 83

To evaluate the sensitivity and specificity of quantitative EEG (QEEG) spectrograms in order to distinguish epileptic from non-epileptic events. Seventeen patients with paroxysmal non-epileptic events, captured during EEG monitoring, were retrospectively assessed using QEEG spectrograms. These patients were compared to a control group of 13 consecutive patients (ages 25-60 years) with epileptic seizures of similar semiology. Assessment of raw EEG was employed as the gold standard against which epileptic and non-epileptic events were validated. QEEG spectrograms, available using Persyst 12 EEG system integration software, were each assessed with respect to their usefulness to distinguish epileptic from non-epileptic seizures. The given spectrogram was interpreted as indicating a seizure if, at the time of the clinically identified event, it showed a visually significant change from baseline. Eighty-two clinically identified paroxysmal events were analysed (46 non-epileptic and 36 epileptic). The "seizure detector trend analysis" spectrogram correctly classified 33/46 (71%) non-epileptic events (no seizure indicated during a clinically identified event) vs. 29/36 (81%) epileptic seizures (seizure indicated during a clinically identified event) (p=0.013). Similarly, "rhythmicity spectrogram", FFT spectrogram, "asymmetry relative spectrogram", and integrated-amplitude EEG spectrogram detected 28/46 (61%), 30/46 (65%), 22/46 (48%) and 27/46 (59%) non-epileptic events vs. 27/36 (75%), 25/36 (69%), 25/36 (69%) and 27/36 (75%) epileptic events, respectively. High sensitivities and specificities for QEEG seizure detection analyses suggest that QEEG may have a role at the bedside to facilitate early differentiation between epileptic seizures and non-epileptic events in order to avoid unnecessary administration of antiepileptic drugs and possible iatrogenic consequences.
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PMID:Assessing quantitative EEG spectrograms to identify non-epileptic events. 2872 36


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