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.
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Acknowledgments
We thank AUSA Shenzhen Inc for data collection. This work is supported by the 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, 111 Project, No. D17019); and Shenzhen Municipal Development and Reform Commission Subject Construction Project [2017] 1434.
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Xie, Y., Yang, H., Yuan, X. et al. Stroke prediction from electrocardiograms by deep neural network. Multimed Tools Appl 80, 17291–17297 (2021). https://doi.org/10.1007/s11042-020-10043-z
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DOI: https://doi.org/10.1007/s11042-020-10043-z