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Epilepsy is one of the most common and diverse set of chronic neurological disorders characterized by an abnormal excessive or synchronous neuronal activity in the brain that is termed “seizure”, affecting about 50 million individuals worldwide. Electroencephalogram (EEG) signal processing technique plays a significant role in detection and prediction of epileptic seizure. Recently, many research...
Epilepsy is characterized by the sudden and recurrent neuronal firing in the brain. It can be detected by analyzing Electroencephalogram (EEG) of the subject. In this paper, a method of classification of EEG signals into normal and seizure classes is presented. Features based on the statistical distributions were calculated for each frame of EEG signals. After ranking the features using Fisher's discriminant...
Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable...
A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate preictal from interictal ECoG signals. Spectral power of ECoG processed in four different fashions are used as features: raw, time-differential, space-differential, and time/space-differential ECoG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology...
Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using...
The electroencephalogram is an attractive clinical tool given its non-invasive nature, its ability to reflect real-time changes in local cortical activity, and the load of objective bioelectrical measurements that can be derived from it. For decades, the electroencephalogram has been successfully used for diagnosing epilepsy and schizophrenia, among other brain disorders. This paper focuses in the...
Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of non-linearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether quadratic representations yield more accurate classifiers...
Epilepsy is a neurological disorder, which sometimes cannot be successfully treated. We propose a real-time closed-loop monitoring and controlling device for epileptic seizure detection and suppression. This wireless-networked embedded device includes signal conditioning circuitry, a stimulator, and a microcontroller with a wireless transceiver. A TI CC2430 receives the conditioned EEG signals and...
In this paper, the automated diagnostic systems trained on diverse and composite features were presented for detection of electrocardiographic changes in partial epileptic patients. In practical applications of pattern recognition, there are often diverse features extracted from raw data that require recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general...
This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted...
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