训练数据大小对使用长短期记忆模型实时呼吸预测的影响
Wenzheng Sun1, Jun Dang2, Lei Zhang3
1Department of Radiation Oncology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China.
Radiation oncology (London, England)
|June 6, 2025
概括
确定最佳训练数据集大小 (TDS) 对于使用长短期记忆 (LSTM) 模型准确预测呼吸非常重要. 90秒的TDS为呼吸信号,平衡性能和数据要求提供了有利的预测准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 准确的呼吸预测对于各种医疗应用至关重要,包括呼吸监测和治疗.
- 长期短期记忆 (LSTM) 模型在分析呼吸等复杂的生理信号方面表现有前途.
研究的目的:
- 用LSTM模型确定最佳训练数据集大小 (TDS),以最大限度地提高呼吸预测的准确性.
- 调查TDS和预测准确性之间的关系,考虑诸如预测时间和测试数据长度等因素.
主要方法:
- 通过实时位置管理系统获得的151名患者的呼吸信号的回顾性分析.
- 通过计算根平均平方误差 (RMSE) 使用八种不同的TDS (10s到150s) 评估LSTM模型预测准确度.
- 在各种TDS中评估超参数相互作用,包括提前时间和测试数据长度.
主要成果:
- 通过RMSE测量的LSTM模型的预测准确性,随着TDS增加到150s,得到了改善.
- 当TDS超过90秒时,预测准确度稳定.
- 最低的0.119厘米的RMSE是在150秒的TDS中获得的,520毫秒的领先时间.
结论:
- 训练数据集的大小显著影响LSTM模型的呼吸信号预测准确度.
- 在TDS和预测准确性之间的关系是非线性的.
- 90秒的TDS被认为是最佳的,在预测准确性和呼吸信号预测数据要求之间提供了有利的平衡.
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