Browsing by Author "T, Christopher"

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CLASSIFICATION OF HEART RATE DATA USING BFO-KFCM CLUSTERING AND IMPROVED EXTREME LEARNING MACHINE CLASSIFIER
(Sri Sakthi Institute of Enginerring and Technology, 2016-01-07) R, Kavitha; T, Christopher
The Electrocardiogram is a tool used to access the electrical recording and muscular function of the heart and in last few decades it is extensively used in the investigation and diagnosis of heart related diseases. It must be noted that the heart rate fluctuates not only because of cardiac demand, however is also influenced as a result of the occurrence of cardiac disease and diabetes. In addition, it has been shown that Heart Rate Variability (HRV) may well be utilized as an early indicator of cardiac disease susceptibility and the existence of diabetes. As a result, the HRV can be exercised for early clinical test of these diseases. Most existing systems make use of Support Vector Machine (SVM), owing to the generalization performance, it is not sufficient for the accurate classification of heart rate data. In order to overcome this complication, Improved Extreme Learning Machine (IELM) classifier is used, to obtain the best parameter value and best feature subset through the use of Bacterial Foraging Optimization (BFO) that feed the classifier. Here in this work, features of linear and nonlinear are extracted from the HRV signals. Following the preprocessing, feature extraction is done effectively together with feature selection with the assistance of BFO for the purpose of data reduction. Subsequently, proposed a scheme to integrate Kernel Fuzzy C-Means (KFCM) clustering and classifier to adequately enhance the accuracy result for ECG beat classification. The accuracy result for classification of heart rate data is shown in the proposed scheme.
CLASSIFICATION OF HEART RATE USING BACK PROPAGATION NEURAL NETWORKS
(Science and Research support Center, Republic of Korea, 2015) R, Kavitha; T, Christopher
A condition of abnormal electrical activity in the heart which is a threat to humans is shown by this electrocardiogram. It is a representative signal containing information about the condition of the heart. The of the P-QRS-T wave shape and size and their time intervals between its various peaks these are all contain useful information about the nature of disease affecting the heart. This paper presents a technique to examine electrocardiogram (ECG) signal, take out the features for the heart beats classification. Collect data from MIT-BIH database. The heart rate is used as the base signal from which certain parameters are extracted and presented to the BPN for classification.
AN EFFECTIVE CLASSIFICATION OF HEART RATE DATA USING PSO-FCM CLUSTERING AND ENHANCED SUPPORT VECTOR MACHINE
(IJST, 2015) R, Kavitha; T, Christopher
Background/Objectives: Heart Rate Variability is an essential feature which decides the condition of human heart. ECG is used as diagnostic tool to access the electrical function of the heart. Methods/Statistical Analysis: The nine linear and nonlinear features are derived from the HRV signals. The feature extraction is carried out with the help of Particle Swarm Optimization (PSO) for data reduction. In proposed scheme Fuzzy C-Means (FCM) clustering and classifier integrated to enhance the accuracy result for ECG beat classification. Findings: The Enhanced SVM classifier classifies the heart rate data. Enhanced SVM classifier groups the linear and non-linear parameters as inputs, which are derived from the HRV signal. The denoise signals are classified and identifies the pattern for better classification of ECG signal. Application/Improvements: The proposed scheme is experimented with the assistance of the most commonly used MIT-BIH arrhythmia database and adequate results were obtained with an accuracy level of 98.38% than the other well-known approaches
HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION.
(ICTACT, 2017) R, Kavitha; T, Christopher
The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG) impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV) is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self-Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN) to be able to increase the accuracy.
A STUDY ON ECG SIGNAL CLASSIFICATION TECHNIQUES
(Foundation of Computer Science, 2014-01-01) R, Kavitha; T, Christopher
The abnormal condition of the electrical activity in the heart is using electrocardiogram shows a threat to human beings. It is a representative signal containing information about the condition of the heart. The P-QRS-T wave shape, size and their time intervals between its various peaks contain useful information about the nature of disease affecting the heart. This paper presents a technique to examine electrocardiogram (ECG) signal, by taking the features form the heart beats classification. ECG Signals are collected from MIT-BIH database. The heart rate is used as the base signal from which certain parameters are extracted and presented to the network for classification. This survey provides a comprehensive overview for the classification of heart rate