MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning

He, Ziyang; Yuan, Zhiyong; An, Panfeng; Zhao, Jianhui; Du, Bo

doi:10.1016/j.cmpb.2021.106379

Cited by 22 publications

(4 citation statements)

References 25 publications

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“…In order to achieve a trade-off between computational burden and information retention, all the ECG signals were downsampled to 250 Hz as in the existing studies [ 23 , 27 , 29 ]. In addition, Daubechies 6 wavelet filtering [ 44 ] was adopted to remove noise and baseline wander in the ECG signals.…”

Section: Methodsmentioning

confidence: 99%

“…For the MBN skeleton, the role of the branch networks is to learn the critical features of each lead. Unlike the conventional MBNs [ 26 , 27 , 28 , 29 ] that synchronously train all the branch networks, a separate scheme was utilized here. It makes the model more flexible and reusable since the multi-layer features learned by different branch networks can arbitrarily combine without any extra training.…”

Section: Methodsmentioning

confidence: 99%

“…The level limitation can reduce the information redundancy and enhance the robustness of the algorithm. Moreover, the features from the final GAP layer are usually critical for the final classification [ 26 , 27 , 28 , 29 ], which correspond to the top level (17th level) of the branch network. Thus, the top level was also considered in the optimization.…”

Section: Methodsmentioning

confidence: 99%

“…Although the models using single lead or multiple leads (<12 leads) can produce accurate MI detection according to the experiment results, limited lead information may prevent these models from extending to a more complex application in the real-world [ 25 ]. Using all of the 12 leads, MFB-CNN, MFB-CBRNN, ML-ResNet, and MFB-LANN were proposed in [ 26 , 27 , 28 , 29 ], respectively. The MI detection accuracies were all greater than 93% in these three studies.…”

Section: Introductionmentioning

confidence: 99%

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EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Liu

Chang

et al. 2021

Biosensors

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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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