基于Mamba的原型对比式学习与增强特征分离用于常见和罕见心律失常的分类
IEEE journal of biomedical and health informatics
|March 3, 2026
概括
本研究介绍了一种基于Mamba的框架,用于使用心电图 (ECG) 诊断罕见心律失常. 这种方法可以提升心血管疾病的早期检测,即使数据有限,也可以改善患者的预后.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 早期诊断心律失常对于心血管健康至关重要.
- 心电图 (ECG) 是标准的诊断工具.
- 由于数据有限,诊断罕见的心律失常是具有挑战性的.
研究的目的:
- 开发一个框架,用ECG诊断常见和罕见的心律失常.
- 为了应对罕见疾病分类数据有限的挑战.
- 为了提高计算机辅助诊断 (CAD) 对心律失常的准确性.
主要方法:
- 提出了一个基于Mamba的原型对比学习框架 (MST-PCAS).
- 使用基于Mamba的空间时间特征融合网络 (MST) 进行心电图建模.
- 实施原型对比学习与增强特征分离 (PCAS) 进行增强分类.
主要成果:
- 在PTBXL (79.13%) 和Chapman (50.72%) 数据集上实现了卓越的稀有类识别准确性.
- 在一般化的Few-Shot Learning (FSL) 设置中证明了有效性.
- 成功确定了常见和罕见的心律失常类别.
结论:
- 该MST-PCAS框架有效地诊断心律失常,包括罕见的类型.
- 这种方法显著改善了在心电图分析中罕见类的识别.
- 这项研究提供了一个有希望的解决方案,以挑战医疗诊断中的少数射击学习问题.
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