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Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90. [Copyright &y& Elsevier]