IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
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Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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The Journal of Machine Learning Research
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term Holter recording. In this paper, we introduce a novel disease-specific feature selection method which consists of a one-versus-one (OvO) features ranking stage and a feature search stage wrapped in the same OvO-rule support vector machine (SVM) binary classifier. The proposed method differs from traditional approaches in that it focuses on the selection of effective feature subsets for distinguishing a class from others by making OvO comparison. The electrocardiograms (ECG) from the MIT-BIH arrhythmia database (MIT-BIH-AR) are used to evaluate the proposed feature selection method. The ECG features adopted include inter-beat and intra-beat intervals, amplitude morphology, area morphology and morphological distance. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH-AR are grouped into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The division of training and testing data complies with the inter-patient schema. Experimental results show that the average classification accuracy of the proposed feature selection method is 86.66%, outperforming those methods without feature selection. The sensitivities for the classes N, S, V and F are 88.94%, 79.06%, 85.48% and 93.81% respectively, and the corresponding positive predictive values are 98.98%, 35.98%, 92.75% and 13.74% respectively. In terms of geometric means of sensitivity and positive predictivity, the proposed method also demonstrates better performance than other state-of-the-art feature selection methods.