Abstract
Physiological signals are bioelectrical signals generated by human organ interactions. These signals can timely reflect the real health status of the human body. With great success in various tasks, there are high expectations for deep learning networks to improve clinical practice. In this paper, we develop a deep neural network to automatically classify arrhythmias recorded by 12-lead electrocardiogram (ECG). And several experiments were conducted on a 12-lead ECG dataset to prove the effectiveness of the method. The mean F1 score of the model is 83.1%, which is 13% higher than the conventional VGG-60. Finally, we interpret the behavior of the model at the patient levels with the SHapley Additive exPlanations (SHAP) method to increase the interpretability of the model.
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Xu, T., Sun, L., Subramani, S., Wang, Y. (2022). Intelligent Interpretation and Classification of Multivariate Medical Time Series Based on Convolutional Neural Networks. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_27
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DOI: https://doi.org/10.1007/978-3-031-20627-6_27
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