1. Stroke prediction from electrocardiograms by deep neural network
- Author
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Yifeng Xie, Xi Yuan, Ruitao Zhang, Peiwu Qin, Qianyun Zhu, Chengming Yang, Chenggang Yan, Hongnan Yang, Zhenhai Chu, and Qian He
- Subjects
Artificial neural network ,Computer Networks and Communications ,Computer science ,Heart malformation ,business.industry ,Deep learning ,Brain dysfunction ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,medicine.disease ,Convolutional neural network ,Hardware and Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Media Technology ,medicine ,cardiovascular diseases ,Artificial intelligence ,business ,Stroke ,Software - Abstract
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.
- Published
- 2020
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