Automatic identification of epileptic and background EEG signals using frequency domain parameters

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%

Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection

Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers

Pre-Ictal Phase Detection with SVMs

Over 50 million persons worldwide are affected by epilepsy. Epilepsy is a brain disorder known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologist diagnose and determine the patient’s treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that may correspond to epilepsy. The objective of this paper is to implement a novel detection technique of pre-ictal state that announces epileptic seizures from the online EEG data analysis. Unlike most published methods, that are aimed to distinguish only the normal from the epilepsy state, in this work the pre-ictal state is introduced as a new patient status, thus differentiating three possible states: normal (healthy), pre-ictal and epileptic seizure. In this manner, the patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work is partially supported by the Ministry of Education and Science of Spain under contract TIN2010-16144 and Junta de Andalucía under contract TIC-1692

Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network

AbstractComputer Aided Diagnosis (CAD) is used to assist radiologist in classifying various type of breast cancers. It already proved its success not only in reducing human error in reading the mammograms but also shows better and reliable classification into benign and malignant abnormalities.This paper will report and attempt on using Radial Basis Function Neural Network (RBFNN) for mammograms classification based on Gray-level Co-occurrence Matrix (GLCM) texture based features.In this study, normal and abnormal breast image used as the standard input are taken from Mammographic Image Analysis Society (MIAS) digital mammogram database. The computational experiments show that RBFNN is better than Back-propagation Neural Network (BPNN) in performing breast cancer classification. For normal and abnormal classification, the result shows that RBFNN's accuracy is93.98%, which is 14% higher than BPNN, while the accuracy of benign and malignant classification is 94.29% which is 2% higher than BPNN