图像分解增强的深度学习用于检测心脏动中转子核心
IEEE transactions on bio-medical engineering
|July 13, 2023
概括
这项研究引入了使用集体实证模式分解 (EEMD) 和你只看一次 (YOLO) 的AI框架,用于精确检测心房动 (AF) 中的转子核心. EEMD-YOLO模型在识别心律失常的这些关键驱动因素方面表现出高精度和回忆力.
科学领域:
- 计算心脏病学计算心脏病学
- 医学中的人工智能
- 心脏电生理学心脏电生理学
背景情况:
- 旋转器,螺旋波重新进入心脏组织,是心房动 (AF) 的关键驱动因素.
- 传统的转子检测方法需要心脏生理学和电图解读方面的专业知识.
- 人工智能和智能传感方面的进步为诊断心律不整,如AF等提供了新的途径.
研究的目的:
- 开发一个人工智能驱动的框架,用于在心脏组织中自动识别旋转芯.
- 使用图像分解技术与深度学习相结合,增强转子检测.
- 在模拟和实验光学映射数据上验证框架.
主要方法:
- 综合实证模式分解 (EEMD) 用于分解心脏成像数据.
- 最具代表性的分解组件被一个只看一次的物体检测模型 (YOLO) 处理.
- EEMD-YOLO框架是使用双域模拟数据和子心脏的光学映射数据进行训练和验证的.
主要成果:
- 集成的 EEMD-YOLO 模型在转子核心检测方面实现了高精度.
- 模拟数据的精度达到97.2%,光学映射数据的精度达到96.8%.
- 模拟数据的回忆率为93.8%,光学映射数据为92.2%,F1分数分别为95.5%和94.4%.
结论:
- EEMD-YOLO框架提供了准确和自动化的转子核心检测.
- 该模型的性能与文献中确定的黄金标准方法相提并论.
- 这种人工智能方法有可能显著帮助诊断和治疗心房动.
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