Fed-CL-一个使用心电图信号的心房动预测系统,采用联合学习机制
Fayez Saud Alreshidi1, Mohammad Alsaffar2, Rajeswari Chengoden3
1Department of Family and Community Medicine, College of Medicine, University of Ha'il, Ha'il, Saudi Arabia.
Scientific reports
|September 9, 2024
概括
联合学习 (FL) 通过深度学习提高了对心房 (AFib) 的预测,同时保护了患者的隐私. 我们的Fed-CL方法结合了LSTM和CNN,以准确检测AFib和分析心率波动.
科学领域:
- 人工智能的人工智能
- 心脏病学 心脏病学
- 数据 隐私 数据 隐私 数据
背景情况:
- 深度学习模型在预测心房动 (AFib) 的ECG和生命体征方面表现有前途.
- 这些数据集中的敏感患者数据引发了严重的隐私问题.
- 与用户隐私平衡预测准确性是一个关键的挑战.
研究的目的:
- 引入联邦学习 (FL) 作为AFib预测的隐私保护技术.
- 为了介绍Fed-CL,一种先进的方法,将LSTM和CNN结合起来,以准确检测AFib.
- 探索平均心率可变性分析在区分正常和异常心律的有用性.
主要方法:
- 实施联合学习 (FL) 以培训模型而不集中敏感数据.
- 开发Fed-CL,集成长期短期记忆 (LSTM) 和卷积神经网络 (CNN).
- 对平均心率变化 (HRV) 的分析,以增强节奏分类.
主要成果:
- 联储-CL方法使用保护隐私的FL框架证明了准确的AFib预测.
- 集成HRV分析为区分心律提供了额外的见解.
- 该系统旨在为医疗保健专业人员提供及时警报和可操作数据.
结论:
- 联合学习有效地解决了AFib预测中的隐私问题.
- 联邦-CL模型为早期AFib检测提供了一种强大而准确的方法.
- 将深度学习与HRV分析相结合,可以促进主动的心血管护理.
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