Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

Bhardwaj, Pooja; Choudhary, Rahul Raj; Dayama, Ravindra

doi:10.5120/4575-6742

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…These val- Table 7 Accuracies, sensitivities and specificities (in 0.1%) for MIT-BIH Arrhythmia Dataset following AAMI recommendations (5 classes). Features ues were reported in the literature in Bhardwaj, Choudhary, and Dayama (2012), in which is analyzed a graph between accuracy and cost keeping gamma constant, and, further, accuracy v/s gamma keeping C constant. When the parametrization of this values is considered in the train phase, the SVM classifier is much slower than the OPF and Bayesian classifiers, as can be seen in Papa et al (2009), Guilherme et al (2011), Papa et al (2013), Santos et al (2012), Ramos et al (2011), for example.…”

Section: Efficiencymentioning

confidence: 99%

ECG arrhythmia classification based on optimum-path forest

Luz

Nunes

Albuquerque

et al. 2013

Expert Systems with Applications

158

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An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graphbased pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.

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Section: Efficiencymentioning

confidence: 99%

ECG arrhythmia classification based on optimum-path forest

Luz

Nunes

Albuquerque

et al. 2013

Expert Systems with Applications

158

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“…The classification of arrhythmias is very important as some arrhythmias are severely fatal while others are not. The detection of arrhythmia is an important task in clinical reasons which can initiate life saving operations [4]. Several methods for automated arrhythmia detection have been developed in the past few decades to attempt simplify the monitoring task [5].…”

Section: Related Reasearch Workmentioning

confidence: 99%

Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data

Jadhav¹,

Nalbalwar²,

Ghatol³

2012

IJCA

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Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately causes irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this paper we proposed an Artificial Neural Network (ANN) based cardiac arrhythmia disease diagnosis system using standard 12 lead ECG signal recordings data. In this study, we are mainly interested in classifying disease in normal and abnormal classes. We have used UCI ECG signal data to train and test three different ANN models. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. ANN models are trained by static backpropagation algorithm with momentum learning rule to diagnose cardiac arrhythmia. The classification performance is evaluated using measures such as mean squared error (MSE), classification specificity, sensitivity, accuracy, receiver operating characteristics (ROC) and area under curve (AUC). Out of three different ANN models Multilayer perceptron ANN model have given very attractive classification results in terms of classification accuracy and sensitivity of 86.67% and 93.75% respectively while Modular ANN have given 93.1% classification specificity.

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“…Mediante la selección de las características en cada nodo, es posible desarrollar un multiclasificador de arritmias asimétrico que presente buenos resultados en comparación con [24][25][26][27][28][29][30][31], con un aumento en la predicción de positivos y disminuyendo el tiempo de ejecución por muestra en comparación con un multiclasificador simétrico. Para el desarrollo de un multiclasificador asimétrico la construcción de la matriz de características es muy importante para mejorar el rendimiento del sistema de clasificación, siendo el método empírico el que mejores resultados presentó, sin embargo, el tiempo que se tarda en determinar la mejor combinación de características es muy superior al tiempo que se tarda usando .…”

Section: Conclusionesunclassified

Proposal of asymmetric multi-classifier of arrhythmias

Mora

Amaya

2012

2012 XXXVIII Conferencia Latinoamericana en Informatica (CLEI)

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This paper presents a new methodology for the development of multi-classifiers SVM with One-Againts-One (OAO), which allows each node to use different features or attributes to differentiate each pair of classes, called asymmetric OAO-SVM. We evaluated this method by developing a classification system to identify four types of arrhythmias (Atrial Fibrillation, Atrial Flutter, Supraventricular Tachyarrhythmia and Ventricular Tachycardia) and Normal ECG, using nonlinear characteristics such as Shannon entropy and Lempel-Ziv complexity.This method presents a positive prediction of 90.72% which represent an improve with respect a typical multi-classifier OAO-SVM.

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Analysis and Classification of Cardiac Arrhythmia Using ECG Signals

Cited by 15 publications

References 9 publications

ECG arrhythmia classification based on optimum-path forest

ECG arrhythmia classification based on optimum-path forest

Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data

Proposal of asymmetric multi-classifier of arrhythmias

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