使用混合深度学习模型检测和分类心电图
Immaculate Joy Selvam1, Moorthi Madhavan2, Senthil Kumar Kumarasamy3
1Department of Electronics and Communication Engineering, Saveetha Engineering College, Thandalam, Chennai, 602105, India.
概括
结合卷积神经网络 (CNN) 和变异自编码器 (VAE) 的新型混合深度学习 (DL) 模型显著改善了从心电图 (ECG) 数据中对心血管疾病 (CVD) 的分类,实现了高精度.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 电心电图 (ECG) 对于诊断心血管疾病至关重要.
- 从ECG中精确的特征提取对于可靠的自动分类至关重要.
- 现有的方法需要改进,以进行可靠的心血管疾病检测.
研究的目的:
- 开发和评估一种混合深度学习 (DL) 模型,以加强基于心电图的心血管疾病分类.
- 提高ECG信号自动解释的准确性和可靠性.
- 将拟议模型的性能与其他DL技术进行比较.
主要方法:
- 采用了混合DL方法,整合了卷积神经网络 (CNN) 和变异自编码器 (VAE) 架构.
- 该模型在广泛的PTB-XL数据集上进行了训练和验证,包括来自18869名患者的21,799个12ECG.
- 对五个超级类和23个心血管疾病子类的分类性能进行了评估.
主要成果:
- 在CNN-VAE模型实现了98.51%的峰值准确度,98.12%的特异性和97.9%的灵敏度.
- 记录了97.95%的优秀F1得分,以及最小的虚假阳性 (2.07%) 和虚假阴性 (1.87%) 率.
- 结果与专家心脏病学家提供的注释进行了验证.
结论:
- 与其他DL方法相比,拟议的CNN-VAE模型在CVD分类中表现出优异的性能.
- 这种混合架构为早期CVD检测和治疗指导提供了有前途的工具.
- 该模型能够更好地描述复杂的心电图信号,这有助于改善临床决策.
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