Comput. Biol. Med. - Heartbeat classification using disease-specific feature selection.

Tópicos

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Resumo

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.

Resumo Limpo

automat heartbeat classif import techniqu assist doctor identifi ectop heartbeat longterm holter record paper introduc novel diseasespecif featur select method consist oneversuson ovo featur rank stage featur search stage wrap ovorul support vector machin svm binari classifi propos method differ tradit approach focus select effect featur subset distinguish class other make ovo comparison electrocardiogram ecg mitbih arrhythmia databas mitbihar use evalu propos featur select method ecg featur adopt includ interbeat intrabeat interv amplitud morpholog area morpholog morpholog distanc follow recommend advanc medic instrument aami heartbeat sampl mitbihar group four class name normal bundl branch block n supraventricular ectop s ventricular ectop v fusion ventricular normal f divis train test data compli interpati schema experiment result show averag classif accuraci propos featur select method outperform method without featur select sensit class n s v f respect correspond posit predict valu respect term geometr mean sensit posit predict propos method also demonstr better perform stateoftheart featur select method

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