将差异信号集成到RNN中,用于实时呼吸系统预测
IEEE journal of biomedical and health informatics
|September 29, 2025
概括
本研究引入了一种使用差异信号和循环神经网络的新实时呼吸预测模型. 该模型通过更好地捕捉呼吸系统动态来提高医疗程序的准确性.
科学领域:
- 医学成像和干预医学成像和干预
- 生物医学工程 生物医学工程
- 计算生理学计算生理学
背景情况:
- 患者的呼吸会导致病变的运动,影响肺结节穿刺和腹部放射治疗的准确性.
- 目前的呼吸预测方法缺乏实时能力,并与动态呼吸信号特征作斗争,导致不准确.
研究的目的:
- 开发一种新的,实时的呼吸预测模型,使用循环神经网络框架.
- 通过准确预测患者的呼吸运动来提高外科手术的精度.
主要方法:
- 开发了一种循环神经网络模型,该模型包含呼吸信号及其第一和第二顺序衍生物.
- 该模型利用差异信号来捕捉高频呼吸特征和长期依赖.
- 解决了处理非线性,准周期性和非静止性呼吸信号的局限性.
主要成果:
- 在200,400和600 ms的预测窗口中实现了卓越的性能.
- 报告的平均绝对误差分别为0.026,0.216和0.394.
- 报告的根平均平方误差分别为0.034,0.288和0.534.
结论:
- 这种新型模型显著提高了实时呼吸预测的精度.
- 不同信号在预测准周期性生理模式 (如呼吸) 中发挥着关键作用.
- 这种方法提高了受呼吸运动影响的图像引导医疗干预的准确性和效率.
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