Heartbeat classification using disease-specific feature selection
文献类型：期刊论文
| 作者 | Dong, J (董军)
|
| 刊名 | COMPUTERS IN BIOLOGY AND MEDICINE
 |
| 出版日期 | 2014 |
| 卷号 | 46期号:0页码:79-89 |
| 关键词 | Feature selection Disease specific Heartbeat classification Support vector machine |
| 通讯作者 | Zhang, ZC (Zhang, Zhancheng) |
| 英文摘要 | 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. (C) 2013 Elsevier Ltd. All rights reserved. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000332910900009 |
| 公开日期 | 2015-02-03 |
| 源URL | [http://ir.sinano.ac.cn/handle/332007/1789]  |
| 专题 | 苏州纳米技术与纳米仿生研究所_学科交叉综合研究部_董军团队 |
| 推荐引用方式 GB/T 7714 | Dong, J . Heartbeat classification using disease-specific feature selection[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2014,46(0):79-89. |
| APA | Dong, J .(2014).Heartbeat classification using disease-specific feature selection.COMPUTERS IN BIOLOGY AND MEDICINE,46(0),79-89. |
| MLA | Dong, J ."Heartbeat classification using disease-specific feature selection".COMPUTERS IN BIOLOGY AND MEDICINE 46.0(2014):79-89. |
入库方式： OAI收割
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