通过特征融合神经网络和动态少数派偏差批量权重损失函数来增强ECG心跳分类
Jiajun Cai1, Junmei Song2,3, Bo Peng1,4
1School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, Sichuan, People's Republic of China.
Physiological measurement
|June 27, 2024
概括
这项研究引入了一种新的深度学习方法,用于从心电图 (ECG) 数据中分类失衡的心跳. 该方法增强了少数群体类别的识别,这对于准确的心脏病状况诊断至关重要.
科学领域:
- 心脏病学 心脏病学
- 医疗人工智能 医疗人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 数据通常会因为心跳分类不平衡而带来挑战.
- 在心电图中精确识别少数群体类别对于诊断心脏病至关重要.
- 现有的方法在心电图数据集中存在显著的不平衡.
研究的目的:
- 开发一种新的深度学习方法,用于ECG中不平衡心跳的分类.
- 为了提高少数群体心跳类的准确识别.
- 通过使用心电图数据,提高心脏病诊断能力.
主要方法:
- 提出了一个特征融合神经网络,有三个专门的分支:完整的心电图,局部QRS波和R波信息.
- 实施了一个动态的少数派偏见的批量权重损失函数,以优先考虑少数派类别.
- 该方法侧重于在不改变原始数据分布的情况下提取各种ECG信号特征.
主要成果:
- 在MIT-BIH数据集上取得了平衡的表现,特别是在少数群体中.
- 在患者内和患者间的范式下,对Supraventricular ectopic beat和Fusion beat的高准确度,灵敏度和特异性得到证明.
- 具体指标包括关键少数节拍的准确性>99%的内科和>96%的患者间情景.
结论:
- 建议的深度学习方法有效地解决了ECG数据集中的类不平衡问题.
- 融合结构和适应性损失功能显著提高了心跳分类的准确性.
- 这种方法为帮助诊断和治疗心脏病提供了一个有前途的工具.
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