The most frequently diagnosed brain disease is epilepsy, which is characterised by the unexpected onset of frequent seizures. The detection of epilepsy in this paper was established by using the wavelet features Haar, dB2, Symlets (Sym8) and dB4, followed by the Softmax Discriminant Classifier, which uses to detect the epilepsy from the EEG signals. The performance of the wavelet features and classifier is evaluated based on the performance index, specificity, sensitivity, precision, time delay and quality values. Amongthe wavelet features, the sym8 performs better than the other and processed further using the Softmax Discriminant Classifier, which outperforms the 90.93 percent classification accuracy, with a low time delay of 1.991s, the 72.61 percent output index, the most promising result in this work.
This effort examines and likens a collection of active methods to dimensionally reduction and select salient features since the electrocardiogram database. ECG signal classification and feature selection plays a vital part in identifies of cardiac illness. An accurate ECG classification could be a difficult drawback. This effort also examines of ECG classification into arrhythmia kinds. This effort discusses the problems concerned in Classification ECG signal and exploration of ECG databases (MIT-BIH), pre-processing, dimensionally reduction, Feature selection techniques, classification and optimization techniques. Machine learning techniques give offers developed classification accurateness with imprecation of dimensionality.
Cardiac Arrhythmia is one of the serious disorders which are most commonly found among humans larger in number. This study is based on proposing a novel approach for heart (Cardiac) arrhythmia disease classification. Many Machine learning algorithms are implemented for the cardiac arrhythmia classification from which the ECG signal are extracted from MIT-BIH Database. The main objective of this study is to do the classification of ECG signals to the normal and abnormal (Ventricular Tachycardia) category using PSO-SVM optimized with Independent Component Analysis using Genetic Algorithm. The extraction of ECG signal is done with twenty four features consisting of Normal and Abnormal clinical clusters. ECG Signals under these categories are extracted from MIT-BIH Arrhythmia database which is read in terms of P,Q,R,S and T voltage-time parametric signal. Genetic Algorithm and Particle Swarm Optimization together used to enhance the performance of the Support Vector Machine (SVM) classifier. Initially the SVM Classifier is designed and it is optimized by searching for the best parametric value where the discriminate function is tuned to extract the features under the best subsets and as a result the fitness functions which are classified are identified with better optimization. Additionally the PSO-SVM Classifier is allowed to undergo the adaptive mechanism wherein which the optimization factor is allowed to restrict the boundaries of classification of ECG arrhythmia with maximum accuracy by the implementation of Independent Component Analysis Optimization using Genetic Algorithm. The results are experimentally demonstrated with the comparison of PCA, ICA, PSO-SVM with ICA and G-ICA. Sensitivity, Specificity, False Positive Rate, True Positive Rate and Accuracy are the experimental parameters used for the performance metrics comparison to classify for normal and diabetic clinical condition. The parameters yield better results for PSO-SVM-ICA and G-ICA with respect to the above mentioned metrics. The Classification Accuracy is attained with 96% with best optimization strategies by using these hybrid classifiers.
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