Radial basis function neural network for prediction of cardiac arrhythmias based on heart rate time series

Kelwade, J. P.; Salankar, Suresh

doi:10.1109/cmi.2016.7413789

Cited by 27 publications

(17 citation statements)

References 14 publications

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“…A hybrid method for HD prediction was proposed in [7] based on risk factors, where authors presented different data mining and neural network classification technologies used in predicting the risk of occurring heart diseases, and it was shown that classifying the risk level of a person using techniques like K-Nearest Neighbor Algorithm, Decision Trees, Genetic algorithm, Naïve Bayes is high when using more attributes and combinations of above techniques. Computer aided decision support system was presented in [8], and showed a reduction in prediction time for HD dataset, and supervised learning techniques for HD dataset prediction was proposed in [9]. Authors in [10] introduced particle swarm optimization to generate evolutionary values for HD, also good classification accuracy for HD dataset was presented in [11], in the form of a comparative analysis of different machine learning algorithms for diagnosis of heart disease as a survey paper, and it showed the suitability of machine learning algorithms and tools to be used for the analysis of HD, and decision-making process accordingly.…”

Section: Backgroundsmentioning

confidence: 99%

Prediction of heart disease and classifiers’ sensitivity analysis

Mellouli

2020

BMC Bioinformatics

119

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Background: Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K-Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases. Results: It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases. Conclusion: Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.

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Section: Backgroundsmentioning

confidence: 99%

Prediction of heart disease and classifiers’ sensitivity analysis

Mellouli

2020

BMC Bioinformatics

119

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“…Investigation of irregular pattern is mandatory for prevention of heart. HRV (Heart rate variability) analysis is very fruitful in finding heart health [39]. Many approaches has been carried out like ANN with fuzzy, support vector machine and generalized discriminant analysis [40], [41].…”

Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

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“…The segments of the RRITS signals to extract the features are formed. The features such as normalized Low Frequency (nLF) and High Frequency (nHF) power components, SD1/ SD2 ratio, Spectral Entropy (SE), Largest Lyapunov Exponent (LLE) and Hurst exponent (HE) extracted from HRV signal using linear and nonlinear methods are presented to train MLP for better prediction accuracy [12,13,[16][17][18].…”

Section: Methodsmentioning

confidence: 99%

“…Kelwade and Salankar predicted classes of cardiac arrhythmia with MLP and radial basis function Neural (RBFN) network [17,18]. Goshvarpour focused on the Lyapunov Exponents and Entropy features to train Quadratic classifier and compare the result with Fisher and k-Nearest Neighbor (k-NN) classifiers [19].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Jp¹,

Ss²

2017

Int J Swarm Intel Evol Comput

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IntroductionThe graphical depiction of the heart beats in the form of electrical signals is known as electrocardiogram (ECG). The abnormal rhythms of the heart beats are termed as cardiac arrhythmias. Some arrhythmias are life threatening. Therefore there is need to identify the heart conditions of cardiac patients. The identification of cardiac arrhythmias in early stage can save the patients from sudden cardiac arrest.A variation in the consecutive cardiac beats is referred to as heart rate variability (HRV). By means of HRV analysis technique cardiac health can usually be computed. An estimation of HRV is recently being adopted as investigation tool for recognition of heart abnormalities in cardiology. AbstractThe study of Heart rate variability is recently gained momentum for an estimation of heart health. This paper suggests a new approach for enhancement of the prediction accuracy of Multi-Layer Perceptrons (MLP) neural network using improved Particle Swarm Optimization (IPSO) technique. The IPSO computes the weights and biases of MLP for the more accurate prediction of the cardiac arrhythmia classes. This study for heart condition prediction involves selection of Three types of heart signals including Left Bundle Branch Block (LBBB), Normal Sinus Rhythm (NSR), Right Bundle Branch Block (RBBB) from MIT-BIH arrhythmia database, formation of heart rate time series, extraction of features from RR interval time series, implementation of training algorithm and prediction of arrhythmia classes. Several experiments on the proposed training method are carried out to superior the convergence ability of MLP. The experimental results gives comparably better evaluation over gradient based Back-Propagation (BP) learning algorithm.

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Radial basis function neural network for prediction of cardiac arrhythmias based on heart rate time series

Cited by 27 publications

References 14 publications

Prediction of heart disease and classifiers’ sensitivity analysis

Prediction of heart disease and classifiers’ sensitivity analysis

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

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