Automatic Epileptic Seizure Detection Research Articles

Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks

About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings can provide valuable information for understanding the mechanisms behind epileptic disorders. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Wavelet transform (WT) is an effective analysis tool for non-stationary signals, such as EEGs. The line length feature reflects the waveform dimensionality changes and is a measure sensitive to variation of the signal amplitude and frequency. This paper presents a novel method for automatic epileptic seizure detection, which uses line length features based on wavelet transform multiresolution decomposition and combines with an artificial neural network (ANN) to classify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A famous public dataset was used to evaluate the proposed method. The high accuracy obtained for three different classification problems testified the great success of the method.

Confrontations with traces of one’s own activity

In everyday life, situations involving confrontation with the traces of our activities are becoming commonplace, through technologies that objectivate and make available our experiences and conduct. Our "informational ecologies" are increasingly reflexive, which elicits performative effects and a mediated return to self, or a dialectic between subject and object which produces transformations. This is the case, for example, with diabetic diaries, through which patients develop a mastery of their pathology, or with a new automatic epileptic seizure detection device that leads different actors to confront their points of view on the relevant experiential data and inscriptions. In addition, self-confrontation, or confrontation with video/audio/written traces of one's own activity, is a powerful reflexive device, this time set up by the researcher. The observable video traces of the lived activity mediate experiences, to help subjects to remember and to describe in detail the activity carried out or to re-elaborate it, and thus to develop knowledge to act and knowledge about the action. There are two types of objectives here: (1) to describe subjet’s experiences in phenomenological detail in order to gain an elaborate understanding of the activity at hand (e.g. musician-composer, rugby referees); (2) to provoke an analytical and collective elaboration and the emergence of controversies for the development of skills and activity (e.g. construction workers).

Beamformer-aided processing of EEG signals for analysing epileptic seizures

Automatic detection of epileptic seizures, by means of online or offline procedures, is a matter of continuous research that commits many research groups. Electroencephalographic (EEG) detection is affected by interference due to specific electrode interference that can be removed by different techniques, namely, beamforming, proposed in this paper. This technique allows improving epilepsy identification technique without performing a precise selecting of higher-order spectra. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9 10 May 2008, Ottawa, ON, Canada.

Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

In this study, a new scheme was presented for detecting epileptic seizures from electro-encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new scheme was based on approximate entropy (ApEn) and discrete wavelet transform (DWT) analysis of EEG signals. Seizure detection was accomplished in two stages. In the first stage, EEG signals were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of the approximation and detail coefficients were computed. Significant differences were found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with over 96% accuracy. Without DWT as preprocessing step, it was shown that the detection rate was reduced to 73%. The analysis results depicted that during seizure activity EEG had lower ApEn values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. The data was further analyzed with surrogate data analysis methods to test for evidence of nonlinearities. It was shown that epileptic EEG had significant nonlinearity whereas normal EEG behaved similar to Gaussian linear stochastic process.

Automatic detection of epileptic seizure using dynamic fuzzy neural networks

Abstract In this study, a new approach based on neural network and fuzzy logic technologies was presented for detection of epileptic seizure to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A dynamic fuzzy neural network (DFNN) that contains dynamical elements in their processing units is used in the classification of EEG signals. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a DFNN with two discrete outputs: normal and epileptic. Some conclusions concerning the impacts of features on epileptic seizure detection was obtained through analysis of the DFNN. The performance of the DFNN model was evaluated in terms of classification accuracies and the results confirmed that the proposed DFNN classifiers have some potential in detecting epileptic seizures. The DFNN model achieved accuracy rates, which were higher than that of neural network model.

Using simulated neuronal cell models for detection of epileptic seizures in foramen ovale and scalp EEG

Objective: To demonstrate a novel approach for real-time and automatic detection of epileptic seizures in EEG recorded with foramen ovale (Fov) or scalp electrodes. Methods: Our seizure detection method is based on simulated leaky integrate and fire units (LIFU), which are classical simple neuronal cell models. The LIFUs are connected to a signal preprocessing stage and increase their spiking rates in response to rhythmic and synchronous EEG signals as typically occur at the onset and during seizures. Results: We analyzed 22 short-term (10±3 min) and 4 long-term (18±7 h) Fov or scalp EEGs of 10 patients with drug resistant partial epilepsy. Seizures ( n=36) were marked by increases of the LIFUs spiking rates above a preset threshold. The durations of increased spiking rates due to seizures were always longer than 10 s (36±21 s) and allowed separation from artifacts, which caused only short durations (1.2±0.6 s) of high spiking rates. The LIFUs correctly detected all the seizures and produced no false alarms. In the long term Fov EEGs seizure detection occurred before the onset of clinical signs (41±22 s). Conclusions: By using simulated neuronal cell models it is possible to automatically detect epileptic seizures in scalp and Fov EEG with high sensitivity and specificity.

Epileptic activity recognition in EEG recording

We apply Approximate Entropy (ApEn) algorithm in order to recognize epileptic activity in electroencephalogram recordings. ApEn is a recently developed statistical quantity for quantifying regularity and complexity. Our approach is illustrated regarding different types of epileptic activity. In all segments associated with epileptic activity analyzed here the complexity of the signal measured by ApEn drops abruptly. This fact can be useful for automatic recognition and detection of epileptic seizures.

An Adaptive Structure Neural Networks with Application to EEG Automatic Seizure Detection

This paper introduces a new algorithm for adaptively adjusting the structure of a multi-layer back-propagation network. The proposed algorithm belongs to the class of neuron generating strategies as opposed to the class of neuron pruning strategies. Initially a “small” multi-layer perceptron network is selected. The stabilized error is used as an index to determine whether the network needs to generate a new neuron or not. If after a period of learning the error is stabilized, but the error is larger than a desired value, then new neuron(s) is (are) generated. The new neurons are placed at locations that contribute most to the network error behavior through the fluctuation in their input weight vectors. Among the features of the new architecture are its improved performance and generalization capabilities compared to a standard fixed-structure back-propagation network. Application to an electroencephalogram (EEG) automatic epileptic seizure detection is presented to illustrate advantages and capabilities of the proposed algorithm. Using an actual data from five patients it is shown that the proposed approach correctly identifies all true seizures that are also identified by an expert physician. The new algorithm provides a reduction of 60–70% in the training epochs as compared to a back-propagation algorithm. Furthermore, it is shown that by utilizing a new training algorithm it is possible to reduce the false seizure detections to zero while resulting in a 5.1% error in identifying the true seizures. Copyright © 1996 Elsevier Science Ltd