使用合成心电图来训练神经网络的域随机化.
Matti Kaisti1, Juho Laitala1, David Wong2
1Department of Computing, Digital Health Lab, University of Turku, Turku 20500, Finland.
Artificial intelligence in medicine
|September 6, 2023
概括
本研究介绍了合成心电图 (ECG) 数据生成用于训练神经网络. 合成心电图中的域随机化实现了与真实数据相比或比真实数据更好的性能,增强了模型的概括性.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 信号处理 信号处理
背景情况:
- 可穿戴的单导电心电图 (ECG) 监测器产生重要的心脏数据.
- 训练强大的神经网络进行心电图分析往往需要大型,多样化,准确标记的现实世界数据集.
- 挑战包括数据隐私,阶级不平衡以及数据采集成本.
研究的目的:
- 开发一种使用合成心电图数据训练神经网络的方法.
- 评估用合成数据训练的模型与用真实数据训练的模型的性能.
- 为了证明域随机化的有效性,以改善模型概括.
主要方法:
- 合成心电图信号的生成,模仿可穿戴的单显示器.
- 域随机化的应用,波形形状的变化,RR间隔和噪声.
- 使用真实心电图上的r波检测对各种活动和心房动的性能评估.
主要成果:
- 用域随机化合成心电图数据训练的模型实现了与使用真实数据训练的模型相等或更高的性能.
- 在不同的种子和未见的测试组中观察到强大的模型性能.
- 该方法在没有复杂的对抗域适应技术的情况下证明了有效的概括性.
结论:
- 合成心电图数据生成与域随机化为训练强大的神经网络提供了一个可行的,可解释的替代方案.
- 这种方法促进了免费,准确标记的私人培训数据的使用.
- 它允许对域不敏感的心脏病进行分类,并解决数据不平衡问题.
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