Search

Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings.Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach.The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins.Compared with traditional variance-based feature extraction, the proposed SampEn-based feature extraction method not only shows a significant improvement in the accuracy, but also has higher classification robustness and stability in terms of the much lower standard errors of classification accuracies across different evaluation periods. In addition, the proposed classification framework runs around 20 times faster than the support vector machine model during testing.The high accuracy and efficiency of the proposed method makes it feasible to extend it to the development of a real-time EEG-based brain monitoring system for epileptic seizure prediction.

Epilepsy is one of the most common neurological disorders-approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. Until now, reviews of epileptic seizure detection have been published but none of them has specifically reviewed developments of automatic medical support systems utilized for EEG-based epileptic seizure detection. This review aims at filling this lack. The main objective of this review will be to briefly discuss different methods used in this research field and describe their critical properties.

The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. Substantial data is generated by the EEG recordings of ambulatory recording systems, and detection of epileptic activity requires a time-consuming analysis of the complete length of the EEG time series data by a neurology expert. A variety of automatic epilepsy detection systems have been developed during the last ten years. In this paper, we investigate the potential of a recently-proposed statistical measure parameter regarded as Sample Entropy (SampEn), as a method of feature extraction to the task of classifying three different kinds of EEG signals (normal, interictal and ictal) and detecting epileptic seizures. It is known that the value of the SampEn falls suddenly during an epileptic seizure and this fact is utilized in the proposed diagnosis system. Two different kinds of classification models, back-propagation neural network (BPNN) and the recently-developed extreme learning machine (ELM) are tested in this study. Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed.

Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify 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 public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures.

Feature extraction is an important technique for complex, multivariate data containing various attributes. In this paper, we propose new detection schemes to help diagnosing epilepsy and detecting the onset of epileptic seizures. These schemes are based on the dynamic principle component analysis (PCA) approach and on partially extracted features. We propose a detection performance measure for evaluation of performance of the detection schemes. We also introduce a method for determining the threshold of the PC classifier using the normalized partial energy sequence of the extracted features of the training data set. We use partially extracted features to act as a classifier to help diagnosing epilepsy and detecting the onset of epileptic seizures. A publicly available EEG database is employed to evaluate our detection schemes. Our study shows that the proposed detection schemes are very promising in assisting diagnosis of epilepsy and for epileptic seizure detection.