Abstract
Electrocardiogram (ECG) signals are widely used in the medical diagnosis of heart disease. Automatic extraction of relevant and reliable information from ECG signals is a tough challenge for computer systems. This study proposes a novel 12-lead electrocardiogram (ECG) multi-label classification algorithm using a combination of Neural Network (NN) and the characteristic points. The proposed model is an end-to-end model. CNN extracts the morphological features of each ECG. Then the features of all the beats are considered in the context via BiRNN. The proposed method was evaluated on the dataset offered by The First China Intelligent Competition, and results were measured using the macro F1 score of all nine classes. Our proposed method obtained a macro F1 score of 0.878, which is excellent among the competitors.
This work is partially supported by The National Key Research and Development Program of China No. 2017YFB1401804.
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Xia, Z. et al. (2019). Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points. In: Liao, H., et al. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH CVII-STENT 2019 2019. Lecture Notes in Computer Science(), vol 11794. Springer, Cham. https://doi.org/10.1007/978-3-030-33327-0_10
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DOI: https://doi.org/10.1007/978-3-030-33327-0_10
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