Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine
Selcuk University, Electrical and Electronics Engineering Department, 42035 Konya, Turkey Applied Mathematics and Computation
(Impact Factor: 1.55).
03/2007; 186(1):898-906. DOI: 10.1016/j.amc.2006.08.020
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.
Available from: content.iospress.com
- "So far the most common modeling methods are partial least squares (PLS) , support vector machine (SVM)  and back propagation neural network (BPNN) . These methods have been used widely in electrophysiological studies   . "
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ABSTRACT: The objective of this study is to build a fuzzy linguistic prediction model (FLPM) for analyzing the actuation duration of acute hyperglycemia to sinoatrial node field potential. The field potential was recorded using microelectrode arrays (MEA). The experimental data were analyzed using partial least squares (PLS), support vector machine (SVM), back propagation neural network (BPNN) and the proposed method. The experimental results showed that the fuzzy linguistic prediction model could be adopted for predicting the actuation duration of high glucose to the sinoatrial node field potential. Compared with the other aforementioned models, the proposed model had higher prediction accuracy.
Available from: Reza Ebrahimpour
- "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. "
Available from: Babita Pandey
- "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. "
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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
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