Abstract
In this work, we have proposed an electrocardiogram (ECG) arrhythmia classification method for short 12-lead ECG records to identify nine types (one normal type and eight abnormal types), using a 1D densely connected CNN which is a relatively novel convolutional neural network (CNN) model and shows outstanding performance in the field of pattern recognition. Firstly, noticing that ECG records are one dimensional time series with different noise levels, several wavelet-based shrinkage filtering methods were adopted to the ECG records for data augmentation. Secondly, each ECG record was divided into segments with a fixed length of 10 s, and the total number of segments for an ECG record is 10. And then, 10 segments were fed into an optimized 1D densely connected CNN for training. And lastly, a threshold vector was trained for the multi-label classification since each record may have more than one abnormal types. The approach has been validated against The First China ECG Intelligent Competition data set, obtaining a final F1 score of 0.873 and 0.863 on the validation set and test set, respectively.
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References
The First China ECG Intelligent Competition. http://mdi.ids.tsinghua.edu.cn. Accessed 12 July 2019
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Wang, C., Yang, S., Tang, X., Li, B. (2019). A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN. 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_9
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