An Integration Method for ECG Multi-Classification

Chao-Xin Xie, Minghui Fan, Liang-Hung Wang, Pao-Cheng Huang
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Abstract

The application of artificial intelligence to the diagnosis of ECG is of great significance. We combine machine learning algorithm with deep learning algorithm to give full play to the advantages of different algorithms by ensemble learning. Finally, we fuse the selected models so that the accuracy of identifying five kinds of arrhythmias can reach 94%. Particularly, the accuracy of class F beat which is difficult to identify has also been improved.
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一种心电多分类的集成方法
人工智能在心电图诊断中的应用具有重要意义。我们将机器学习算法与深度学习算法相结合,通过集成学习充分发挥不同算法的优势。最后对所选模型进行融合,使五种心律失常的识别准确率达到94%。特别是,难以识别的F类拍的精度也得到了提高。
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