Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods

Recognizing ECG cardiac arrhythmia automatically is an essential task for diagnosing the abnormalities of cardiac muscle. The proposal of few algorithms has been made for classifying the ECG cardiac arrhythmias, however the system of classification efficiency is determined on the basis of its prediction and diagnosis accuracy. Hence, in this study the proposal of an efficient system has been made for classifying the ECG cardiac arrhythmia as an expertise. Discrete Wavelet Transform (DWT) is being utilized for the preprocessing mechanism of ECG signal, Independent Component Analysis (ICA) is being utilized for dimensionality reduction and Feature Extraction process of ECG signal and Multi-Layer Perceptron (MLP) neural network is being utilized for performing the task of classification. As an outcome of classification, the results have been acquired on categorizing Normal Beats under the class of Non-Ectopic beat, Atrial Premature Beat under the class of Supra-Ventricular ectopic beat and Ventricular Escape beat under the class of Ventricular ectopic beat on the basis of standardization given by ANSI/AAMI EC57: 1998. For the acquisition of ECG signal, MIT-BIH physionet arrhythmia database is being utilized in this study added to that its being utilized for training process and testing process of the classifier on the basis of MLP-NN. The results obtained from the simulation has been inferred that the accuracy of classification of the proposed algorithm is 96.50% on utilizing 10 files inclusive of normal beats, Atrial Premature Beat and Ventricular Escape beat.

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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The convolution structure for the Legendre transform developed by Gegenbauer is exploited to define Legendre translation by means of which a new wavelet and wavelet transform involving Legendre Polynomials is defined. A general reconstruction formula is derived. MSC 33A40; 42C10