Seizure Detection In EEG Recordings Research Articles

Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM

The detection of seizure activity in electroencephalogram (EEG) signals is crucial for the classification of epileptic seizures. However, epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. In this work, we present a new technique for seizure classification of EEG signals using Hilbert–Huang transform (HHT) and support vector machine (SVM). In our method, the HHT based time-frequency representation (TFR) has been considered as time-frequency image (TFI), the segmentation of TFI has been implemented based on the frequency-bands of the rhythms of EEG signals, the histogram of grayscale sub-images has been represented. Statistical features such as mean, variance, skewness and kurtosis of pixel intensity in the histogram have been extracted. The SVM with radial basis function (RBF) kernel has been employed for classification of seizure and nonseizure EEG signals. The classification accuracy and receiver operating characteristics (ROC) curve have been used for evaluating the performance of the classifier. Experimental results show that the best average classification accuracy of this algorithm can reach 99.125% with the theta rhythm of EEG signals.

Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks

Epilepsy is the most prevalent neurological disorder in humans after stroke. Recurrent seizure is the main characteristic of the epilepsy. Electroencephalogram (EEG) is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. Thus, EEG is considered an indispensable tool for diagnosing epilepsy in clinic applications. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Multiwavelets, which contain several scaling and wavelet functions, offer orthogonality, symmetry and short support simultaneously, which is not possible for scalar wavelet. With these properties, recently multiwavelets have become promising in signal processing applications. Approximate entropy is a measure that quantifies the complexity or irregularity of the signal. This paper presents a novel method for automatic epileptic seizure detection, which uses approximate entropy features derived from multiwavelet transform and combines with an artificial neural network to classify the EEG signals regarding the existence or absence of seizure. To the best knowledge of the authors, there exists no similar work in the literature. A well-known public dataset was used to evaluate the proposed method. The high accuracy obtained for two different classification problems verified the success of the method.

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.