基于KAN和xLSTM的ECG信号的特征提取和智能诊断
Aihua Li1, Yunjie Lin2, Yuwei Liu2
1School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China; College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, China.
Biosensors & bioelectronics
|October 19, 2025
概括
两种新的深度学习模型,科尔莫戈罗夫-阿诺德网络 (KANs) 和xLSTM,在检测心律失常时从心电图信号中显示出更高的准确性. 这些先进的方法为心血管疾病诊断提供了更好的性能.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 准确及时诊断心律失常对于预防和治疗心血管疾病至关重要.
- 现有的基于心电图的心律失常检测方法在实现高精度方面面临挑战.
研究的目的:
- 提出和评估两种新的深度学习架构,科尔莫戈罗夫-阿诺德网络 (KANs) 和xLSTM,用于从心电图信号中增强心律失常检测.
- 使用标准心律失常分类标准,将KANs和xLSTM的性能与现有的深度学习方法进行比较.
主要方法:
- 开发了具有可学习的spline激活函数的KAN和具有指数级门和修改记忆结构的xLSTM.
- 利用焦点损失函数来解决心电图数据集中的样本不平衡.
- 根据医学仪器进步协会 (AAMI) 标准对心律失常进行分类.
- 在MIT-BIH (109,262个样本) 和圣彼得堡INCART (166,909个样本) 数据库上评估模型.
主要成果:
- 无论是KAN还是xLSTM,都在准确性和F1分数方面表现出卓越的表现,用于分类五种主要心律失常类别.
- 在较低的计算复杂度下,KAN 实现了更高的准确性.
- 与四种流行的深度学习方法相比,xLSTM 的精度提高了约0.41%.
- 在INCART数据库中,KANs和xLSTM分别达到了96.87%和97.15%的准确度,展示了多功能性.
结论:
- KAN和xLSTM代表了基于心电图的心律失常检测深度学习的重大进展.
- 这些模型提供了更高的准确性和效率,有助于及时诊断和管理心血管疾病.
- 在不同数据库中展示的多功能性突显了它们广泛临床应用的潜力.
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