南方科技大学知识苑(SUSTech KC): Stroke prediction from electrocardiograms by deep neural network

题名	Stroke prediction from electrocardiograms by deep neural network
作者	Xie，Yifeng 1; Yang，Hongnan 1; Yuan，Xi 2; He，Qian 2; Zhang，Ruitao 2; Zhu，Qianyun 2; Chu，Zhenhai 3; Yang，Chengming 4; Qin，Peiwu 2; Yan，Chenggang 1
通讯作者	Xie，Yifeng
发表日期	2020
DOI	10.1007/s11042-020-10043-z
发表期刊	MULTIMEDIA TOOLS AND APPLICATIONS 影响因子和分区
ISSN	1380-7501
EISSN	1573-7721
卷号	80页码:17291-17297
摘要	The brain is an energy-consuming organ that heavily relies on the heart for energy supply. Heart abnormalities detected by electrocardiogram (ECG) might provide diagnostic indicators for brain dysfunctions such as stroke. Diagnosis of brain diseases by ECG requires proficient domain knowledge, which is both time and labor consuming. Deep learning is capable of constructing a nonlinear correlation between ECG and stroke without prior expert knowledge. Here, we propose a data-driven classifier-Dense convolutional neural Network (DenseNet) for stroke prediction based on 12-leads ECG data. With our finely-tuned model, we obtain the training accuracy of 99.99% and the prediction accuracy of 85.82%. To our knowledge, this is the first report studying the correlation between stroke and ECG with the aid of deep learning. The results indicate that ECG is a valuable complementary technique for stroke diagnostics.
关键词	Classification Convolutional neural network Electrocardiograms Stroke
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
资助项目	National Key Research and Development Program of China[2017YFC0820605] ; National Natural Science Major Foundation of Research Instrumentation of PR China[61427808] ; Zhejiang Province Nature Science Foundation of China[LR17F030006] ; Zhejiang Province Nature Science Foundation of China (111 Project)[D17019] ; Shenzhen Municipal Development and Reform Commission Subject Construction Project[[2017] 1434]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000583943600001
出版者	SPRINGER
EI入藏号	20204409418134
EI主题词	Convolution ; Forecasting ; Convolutional neural networks ; Deep neural networks ; Classification (of information) ; Domain Knowledge
EI分类号	Ergonomics and Human Factors Engineering:461.4 ; Biomedical Equipment, General:462.1 ; Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Information Sources and Analysis:903.1
ESI学科分类	COMPUTER SCIENCE
Scopus记录号	2-s2.0-85093945376
来源库	Scopus
引用统计	被引频次[WOS]：21
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/209290
专题	南方科技大学医院
作者单位	1.Department of Automation,Hangzhou Dianzi University,Hangzhou,310016,China 2.Center of Precision Medicine and Healthcare,Tsinghua-Berkeley Shenzhen Institute,Shenzhen,518055,China 3.Division of Neurology,Southern University of Science and Technology Hospital,Shenzhen,518055,China 4.Division of Ophthalmology,Southern University of Science and Technology Hospital,Shenzhen,518055,China
推荐引用方式 GB/T 7714	Xie，Yifeng,Yang，Hongnan,Yuan，Xi,et al. Stroke prediction from electrocardiograms by deep neural network[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2020,80:17291-17297.
APA	Xie，Yifeng.,Yang，Hongnan.,Yuan，Xi.,He，Qian.,Zhang，Ruitao.,...&Yan，Chenggang.(2020).Stroke prediction from electrocardiograms by deep neural network.MULTIMEDIA TOOLS AND APPLICATIONS,80,17291-17297.
MLA	Xie，Yifeng,et al."Stroke prediction from electrocardiograms by deep neural network".MULTIMEDIA TOOLS AND APPLICATIONS 80(2020):17291-17297.