基于变压器的时间序列学习器用于心律失常的分类.
Ann Varghese1, Suraj Kamal2, James Kurian2
1Department of Electronics, Cochin University of Science and Technology, Cochin, 682022, Kerala, India. ann.doe@cusat.ac.in.
Medical & biological engineering & computing
|June 6, 2023
概括
本研究介绍了一种基于变压器的模型,用于从心电图 (ECG) 信号中分类心律失常. 这种新的方法在识别心律异常方面取得了很高的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 对于诊断心律失常至关重要.
- 传统和深度学习方法在分类心脏信号方面存在局限性.
- 电脑心电图信号具有时间序列性质,适合高级序列建模.
研究的目的:
- 提出一种基于变压器的模型,用于准确的心电图心律失常分类.
- 为了利用变压器架构的高度并行性来进行心脏信号分析.
- 适应预训练的自然语言处理变压器 (DistilBERT) 用于生物医学信号处理.
主要方法:
- 电脑心电图信号被拒绝,围绕R峰值进行细分,并为数据集平衡进行过量采样.
- 使用了DistilBERT变压器模型,跳过了输入嵌入,使用了位置编码.
- 在变压器编码器输出中添加了一个分类头,用于最终的概率生成.
主要成果:
- 拟议的变压器模型在分类各种心律失常方面表现出色.
- 在MIT-BIH数据集上实现了99.92%的准确性.
- 获得的精度,灵敏度和F1得分为0.99,ROC-AUC得分为0.999.
结论:
- 基于变压器的模型,特别是DistilBERT,对于ECG心律失常的分类非常有效.
- 拟议的方法比传统和现有的深度学习技术提供了有前途的进步.
- 这种方法在改善心律失常的诊断和管理方面具有显著的潜力.
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