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ML-STIM: 机器学习用于下丘脑核的手术内测绘图.

Fabrizio Sciscenti1,2, Valentina Agostini1,2, Laura Rizzi3,4

  • 1Department of Electronics and Telecommunications, Politecnico di Torino, Turin 10129, Italy.

Journal of neural engineering
|July 29, 2025
PubMed
概括

亚thalamic核的机器学习手术内映射 (ML-STIM) 在深度大脑刺激手术期间自动识别亚thalamic核,提高帕金森病患者的准确性和速度.

关键词:
警方 警方 警方 警方在STN-DBS之间.文物检测 发现 发现 发现深度大脑刺激 刺激大脑电极放置位置 电极放置位置多层感知器多层感知器实时分类实时分类.

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科学领域:

  • 神经外科 神经外科
  • 计算神经科学是一种神经科学.
  • 生物医学工程 生物医学工程

背景情况:

  • 亚thalamic Nucleus (STN) 的深度大脑刺激 (DBS) 有效地治疗帕金森病 (PD) 的运动症状.
  • 术内STN识别依赖于微电极记录 (MER),这个过程依赖于操作员,耗时,容易变化.
  • 自动化MER分析对于提高DBS程序的效率和一致性至关重要.

研究的目的:

  • 开发和验证机器学习管道ML-STIM,用于用于手术内STN识别的MER的自动实时分类.
  • 评估ML-STIM在独立数据集中的准确性和通用性.
  • 为了证明拟议的ML管道的计算效率和可解释性.

主要方法:

  • 开发了ML-STIM,一个涉及MER预处理,特征提取和多层感知子分类的管道.
  • 实现了自适应的文物删除算法,以在识别文物时保留STN信号.
  • 使用相关性分析和ReliefF排名选择的MER特征,然后在数据集A (46名患者) 上进行训练和验证,并在数据集B (36名患者) 上进行测试.

主要成果:

  • ML-STIM实现了很高的分类准确度:在数据集A上达到87.8 ± 1.7%,在数据集B上达到83.8 ± 1.6%.
  • 该模型显著优于最先进的深度学习模型 (ResNet-AT,p < 0.01).
  • 实时处理10秒录音的时间为139.4±2.1毫秒,而移除文物显著提高了特异性 (p < 0.001).

结论:

  • ML-STIM提供了一个准确,可解释和计算高效的解决方案,用于手术内STN识别.
  • 该管道证明了对来自不同外科中心的数据的强大通用性.
  • 使用ML-STIM的自动化MER分析有可能简化帕金森病患者的DBS手术.