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: A novel supervised neural network-based algorithm is designed to reliably distinguish in electrocardiographic (ECG) records between normal and ischemic beats of the same patient. The basic idea behind this paper is to consider an ECG digital recording of two consecutive R-wave segments (RRR interval) as a noisy sample of an underlying function to be approximated by a fixed number of Radial Basis Functions (RBF). The linear expansion coefficients of the RRR interval represent the input signal of a feed-forward neural network which classifies a single beat as normal or ischemic. The system has been evaluated using several patient records taken from the European ST-T database. Experimental results show that the proposed beat classifier is very reliable, and that it may be a useful practical tool for the automatic detection of ischemic episodes.Applied Mathematics and Computation. 01/2009;
Conference Proceeding: Machine learning in electrocardiogram diagnosis[show abstract] [hide abstract]
ABSTRACT: The electrocardiogram (ECG) is a measure of the electrical activity of the heart. Since its introduction in 1887 by Waller, it has been used as a clinical tool for evaluating heart function. A number of cardiovascular diseases (CVDs) (arrhythmia, atrial fibrillation, atrioventricular (AV) dysfunctions, and coronary arterial disease, etc.) can be detected non-invasively using ECG monitoring devices. With the advent of modern signal processing and machine learning techniques, the diagnostic power of the ECG has expanded exponentially. The principal reason for this is the expanded set of features that are typically extracted from the ECG time series. The enhanced feature space provides a wide range of attributes that can be employed in a variety of machine learning techniques, with the goal of providing tools to assist in CVD classification. This paper summarizes some of the principle machine learning approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and their classification accuracy.Computer Science and Information Technology, 2009. IMCSIT '09. International Multiconference on; 11/2009
Article: Discrimination of the Heart Ventricular and Atrial Abnormalities via a Wavelet-Aided Adaptive Network Fuzzy Inference System (ANFIS) Classifier[show abstract] [hide abstract]
ABSTRACT: The aim of this study is to address a new feature extraction method in the area of the heart arrhythmia classification based on a metric with simple mathematical calculation called Curve-Length Method (CLM). In the presented method, curve length of the under study excerpted segment of signal is considered as an informative feature in which the effect of important geometric parameters of the original signal can be found. To show merits of the presented method, first the original electrocardiogram (ECG) in lead I is pre-processed by removing its baseline wander then by scaling it in the [-1,1] interval. In the next step, using a trous method, discrete wavelet scales 2 3 and 2 4 and smoothing function scale 2 2 are extracted. Afterwards, segments including samples of the QRS complex, P and T waves are estimated via an approximation criterion and CLM is implemented to extract corresponding features from aforementioned scales, smoothing function and also from each original segment. The resulted feature vector (including 12 components) is used to tune an Adaptive Network Fuzzy Inference System (ANFIS) classifier. The presented strategy is applied to classify four categories found in the MIT-BIH Arrhythmia Database namely as Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC) and average values of Se = 99.81%, P+ = 99.80%, Sp = 99.81% and Acc = 99.72% are obtained for sensitivity, positive predictivity, specifity and accuracy respectively showing marginal improvement of the heart arrhythmia classification performance.