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Highlights? We present a novel automatic technique for epileptic EEG classification into the normal, interictal, and ictal activities. ? We use eigenvalues extracted from wavelet coefficients as features. ? Several supervised learning based classifiers were trained using selected features. ? We demonstrate that our proposed technique can yield 99% classification accuracy. Electroencephalogram (EEG) signals are used to detect and study the characteristics of epileptic activities. Owing to the non-linear and dynamic nature of EEG signals, visual inspection and interpretation of these signals are tedious, time-consuming, error-prone, and subjected to inter-observer variabilities. Therefore, several Computer Aided Diagnostic (CAD) based studies have adopted non-linear techniques to study the normal, interictal, and ictal activities in EEGs. In this paper, we present a novel automatic technique based on data mining for epileptic activity classification. In order to compare our study with the results of relative studies in the literature, we used the widely used benchmark dataset from Bonn University for evaluation of our proposed technique. Hundred samples each in normal, interictal, and ictal categories were used. We decomposed these segments into wavelet coefficients using Wavelet Packet Decomposition (WPD), and extracted eigenvalues from the resultant wavelet coefficients using Principal Component Analysis (PCA). Significant eigenvalues, selected using the ANOVA test, were used to train and test several supervised classifiers using the 10-fold stratified cross validation technique. We obtained 99% classification accuracy using the Gaussian Mixture Model (GMM) classifier. The proposed technique is capable of classifying EEG segments with clinically acceptable accuracy using less number of features that can be extracted with less computational cost. The technique can be written as a software application that can be easily deployed at a low cost and used with almost no expert training. We foresee that this software can, in the future, evolve into an efficient adjunct tool that cannot only classify epileptic activities in EEG signals but also automatically monitor the onset of seizures and thereby aid the doctors in providing better and timely care for the patients suffering from epilepsy.

Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.

Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

An electrocardiogram (ECG) represents the electrical activity of the heart and can be used to investigate cardiac health. Routinely subtle changes in the ECG's P-QRS-T wave are employed to depict a particular type of cardiac abnormality. Herein, we have used heart rate (derived from ECG) as the base signal for our analysis, to demonstrate how effectively we can differentiate cardiac rhythm abnormalities. The normal heart rate varies continuously between 60 to 100 bpm. These signals are highly nonlinear and non-stationary in nature. In this work, we have analyzed 352 subjects from nine different cardiac classes. Hence, we have used nonlinear entropies as features of these heart rate signals. We have then proposed a novel Integrated Index combining these entropies, to diagnose cardiac rhythm abnormalities. This one Index value can now be used to differentiate different cardiac classes.