Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine
ABSTRACT Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause 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 study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50–50% of training-test dataset, 70–30% of training-test dataset and 80–20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.
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ABSTRACT: This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.Computational and Mathematical Methods in Medicine 01/2013; 2013:453402. · 0.79 Impact Factor
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ABSTRACT: In this paper, a novel use of Kernel–Adatron (K–A) learning algorithm to aid SVM (Support Vector Machine) for ECG arrhythmias classification is proposed. The proposed pattern classifier is compared with MLP (multi-layered perceptron) using back propagation (BP) learning algorithm. The ECG signals taken from MIT-BIH arrhythmia database are used in training to classify 6 different arrhythmia, plus normal ECG. The MLP and SVM training and testing stages were carried out twice. They were first trained only with one ECG lead signal and then a second ECG lead signal was added to the training and testing datasets. The aim was to investigate its influence on training and testing performance (generalization ability) plus time of training for both classifiers. Implementation of these three criteria for evaluation of ECG signals classification will ease the problem of structural comparisons, which has not been given attention in previous research works. The results indicate that SVM in comparison to MLP is much faster in training stage and nearly seven times higher in performance, but MLP generalization ability in terms of mean square error is more than three times less. The proposed SVM method shows considerable improvement in comparison to recently reported results obtained by Osowski et al. (2008).Expert Systems with Applications. 01/2010;