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.
"Abbreviations: KNN, K-nearest neighbors; SVM, support vector machine; ECG, electrocardiogram; DWT, discrete wavelet transforms; SNR, signal to noise ratio; ANN, artificial neural network; MEN, maximum epochs number; NHLN, number of hidden layer neurons; RBF, radial basis function; MLP-BP, multi-layer perceptron back propagation; FP, false positive; FN, false negative; TP, true positive; P+, positive predictivity (%); Se, sensitivity (%); CPUT, CPU time; MITDB, MIT-BIH Arrhythmia Database; SMF, smoothing function; FIR, finite-duration impulse response; LBBB, left bundle branch block; RBBB, right bundle branch block; PVC, premature ventricular contraction; APB, atrial premature beat; VE, ventricular escape beat; CHECK#0, procedure of evaluating obtained results using MIT-BIH annotation files; CHECK#1, procedure of evaluating obtained results consulting with a control cardiologist; CHECK#2, procedure of evaluating obtained results consulting with a control cardiologist and also at least with 3 residents. 2008; Osowski, Markiewicz, & Tran Hoai, 2008; Ozbay et al., 2006; Polat, Sahan, & Gune, 2006; Wen, Lin, Chang, & Huang, 2009), parametric and probabilistic classifiers (Bartolo et al., 2001; Polat & Gunes, 2007; Wiggins, Saad, Litt, & Vachtsevanos, 2008), the discrimination goals are followed. Although, in such classification approaches, acceptable results may be achieved, however, due to the implementation of the original samples as components of the feature vector, computational cost and burden especially in high sampling frequencies will be very high and the algorithm may take a long time to be trained for a given database. "
"Other methods include detection of QRS complex using support vector machine (SVM) (Mehta and Lingayat, 2007a, 2007b; Polat and Guneş, 2007). Mehta and Lingayat (2007a) describe a method for the detection of QRS using SVM. "
[Show abstract][Hide abstract] ABSTRACT: Intelligent computing system and knowledge-based system have
been widely used in the diagnosis and classification of ECG based diseases.
Several detection methods of ECG parameters for a particular disease have also
been reported in the literature. But little effort has been made by researchers to
combine both. In this work, an integrated model of rule base system for
generating cases and ANN methods for matching cases in the case base
reasoning model for the interpretation and diagnosis of sinus disturbances (SD)
is developed. The SD is hierarchically structured in terms of their
physio-psycho parameters and ECG based parameters. Cumulative confidence
factor (CCF) is computed at different nodes of hierarchy. The SD considered
are sinus arrest, sinus bradycardia, sinus tachycardia and sinus arrhythmia.
MIT/BIH ECG database is used in the simulation study. The basic objective of
this work is to enhance the computational effort with certain level of efficiency
"do not trap in local minima points and need less training input, various methods of SVM have been adopted for ECG signals classification and proved to be effective    . Although many ECG arrhythmia classification methods show good performance in the laboratory, there are only few techniques gaining popularity in practical applications. "
[Show abstract][Hide abstract] 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 04/2013; 2013:453402. DOI:10.1155/2013/453402 · 0.77 Impact Factor
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