Journal
BIOSENSORS-BASEL
Volume 12, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/bios12010015
Keywords
myocardial infarction (MI); genetic algorithm (GA); electrocardiogram (ECG); convolutional neural networks (CNN); architecture optimization
Funding
- National Natural Science Foundation of China [81971702, 62074116, 61874079]
- Fundamental Research Fund for the Central Universities [2042017gf0052, 2042018gf0045]
- Natural Science Foundation of Hubei Province, China [2017CFB660]
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In this paper, an evolving neural network named EvoMBN is proposed for myocardial infarction (MI) diagnosis, utilizing a genetic algorithm (GA) to automatically learn the optimal MBN architectures and designing a novel Lead Squeeze and Excitation (LSE) block for feature summarization.
Multi-branch Networks (MBNs) have been successfully applied to myocardial infarction (MI) diagnosis using 12-lead electrocardiograms. However, most existing MBNs share a fixed architecture. The absence of architecture optimization has become a significant obstacle to a more accurate diagnosis for these MBNs. In this paper, an evolving neural network named EvoMBN is proposed for MI diagnosis. It utilizes a genetic algorithm (GA) to automatically learn the optimal MBN architectures. A novel fixed-length encoding method is proposed to represent each architecture. In addition, the crossover, mutation, selection, and fitness evaluation of the GA are defined to ensure the architecture can be optimized through evolutional iterations. A novel Lead Squeeze and Excitation (LSE) block is designed to summarize features from all the branch networks. It consists of a fully-connected layer and an LSE mechanism that assigns weights to different leads. Five-fold inter-patient cross validation experiments on MI detection and localization are performed using the PTB diagnostic database. Moreover, the model architecture learned from the PTB database is transferred to the PTB-XL database without any changes. Compared with existing studies, our EvoMBN shows superior generalization and the efficiency of its flexible architecture is suitable for auxiliary MI diagnosis in real-world.
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