Abstract
Myocardial infarction (MI) is a type of cardiovascular diseases (CVDs) with high mortality. Early diagnosis and treatment are crucial to improve survival rate. At present, electrocardiogram (ECG) is a common method for clinical diagnosis of MI, but it requires rich experiences. Hence it is meaningful to design an approach that can screen the MI automatically. In this paper, a feature fusion network is proposed for MI screening based on ECG images, which is composed of heartbeat detection module, local and global feature extraction module, feature fusion and classification module. Firstly, heartbeats are detected from ECG images. Then, heartbeats features are extracted as local features, at the same time, the features extracted from the corresponding ECG image are thought as global features. Finally, classification is designed for judging the input ECG is MI or normal based on fused features. Experiments on two ECG image datasets show the robust performance of the proposed method for MI screening at 99.34% of accuracy, 99.78% of specificity, and 99.64% of sensitivity.
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This work is supported by National Natural Science Foundation of China under grants No.61801428.
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Hao, P., Yin, X., Wu, F., Zhang, F. (2021). A Novel Feature Fusion Network for Myocardial Infarction Screening Based on ECG Images. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_44
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