A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression
文献类型：期刊论文
| 作者 | Yang, Jianli2; Bai, Yang2; Lin, Feng1,2; Liu, Ming2 ; Hou, Zengguang2,3; Liu, Xiuling2
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| 刊名 | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 9期号:10页码:1733-1740 |
| 关键词 | Stacked Sparse Auto-encoders Ecg Arrhythmia Classification Feature Extraction Softmax Regression |
| ISSN号 | 1868-8071 |
| DOI | 10.1007/s13042-017-0677-5 |
| 文献子类 | Article |
| 英文摘要 | Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic cardiovascular disease diagnosis, e.g., to help prevent stroke or sudden cardiac death. However, the complex individual differences in ECG morphology make it challenging in accurately categorizing arrhythmia heartbeats. To promote robustness of the algorithm for individual differences, we propose a novel ECG arrhythmia classification method with stacked sparse auto-encoders (SSAEs) and a softmax regression (SF) model. The SSAEs is employed to hierarchically extract high-level features from huge amount of ECG data. Features are extracted automatically such that no individual difference in feature selection will bias extraction accuracy. Moreover, the input can be reconstructed completely by the features in each level of the auto-encoder. The SF is then trained to serve as a classifier for discriminating six different types of arrhythmia heartbeats. Computational experiments and comparative analyses are presented to validate the effectiveness of the theoretical models. |
| WOS关键词 | PROBABILISTIC NEURAL-NETWORK ; ECG BEAT CLASSIFICATION ; SUPPORT VECTOR MACHINES ; SIGNALS ; OPTIMIZATION ; STATISTICS ; ALGORITHM ; FEATURES ; MIXTURE |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000444006700011 |
| 出版者 | SPRINGER HEIDELBERG |
| 资助机构 | National Natural Science Foundation of China(61203160 ; Natural Science Foundation of Hebei Province(F2015201112) ; 61673158) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27928]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Liu, Xiuling |
| 作者单位 | 1.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore 2.Hebei Univ, Key Lab Digital Med Engn Hebei Prov, Coll Elect & Informat Engn, Baoding, Hebei, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Jianli,Bai, Yang,Lin, Feng,et al. A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2018,9(10):1733-1740. |
| APA | Yang, Jianli,Bai, Yang,Lin, Feng,Liu, Ming,Hou, Zengguang,&Liu, Xiuling.(2018).A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,9(10),1733-1740. |
| MLA | Yang, Jianli,et al."A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 9.10(2018):1733-1740. |
入库方式： OAI收割
来源：自动化研究所
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