用非线性特征识别脑深部电极是否进入丘脑底核，提升手术精准度。
Microelectrode Signal Dynamics as Biomarkers of Subthalamic Nucleus Entry on Deep Brain Stimulation: A Nonlinear Feature Approach
- 通过熵与非线性动力学特征量化神经信号差异
- 模型在测试集上达F1=0.922，AUC=0.941，表现优异
- 适合需要实时辅助定位的神经外科医生使用
精准的术中丘脑底核（STN）定位对帕金森病患者深部脑刺激（DBS）疗效至关重要。尽管微电极记录（MER）提供丰富的电生理信息，当前定位仍依赖主观判断。本研究提出一种定量框架，利用非线性动力学与熵基指标区分STN内/外神经活动。基于三名患者的MER数据，经抗伪影处理、分段与手术标注，提取递归量化分析、非线性及熵特征。采用分层10折交叉验证训练多种监督分类器，并通过配对威尔科克斯符号秩检验结合霍尔姆-博尼费罗尼校正进行统计比较。熵特征与非线性特征组合表现最优，额外树分类器达到0.902±0.027的交叉验证F1分数和0.887±0.055的ROC AUC。最终在20%保留测试集上验证，模型表现稳健（F1=0.922，ROC AUC=0.941），表明非线性与熵信号描述符在支持DBS术中实时决策方面具有潜力。
原文摘要 · Abstract (English)
Accurate intraoperative localization of the subthalamic nucleus (STN) is essential for the efficacy of Deep Brain Stimulation (DBS) in patients with Parkinson's disease. While microelectrode recordings (MERs) provide rich electrophysiological information during DBS electrode implantation, current localization practices often rely on subjective interpretation of signal features. In this study, we propose a quantitative framework that leverages nonlinear dynamics and entropy-based metrics to classify neural activity recorded inside versus outside the STN. MER data from three patients were preprocessed using a robust artifact correction pipeline, segmented, and labelled based on surgical annotations. A comprehensive set of recurrence quantification analysis, nonlinear, and entropy features were extracted from each segment. Multiple supervised classifiers were trained on every combination of feature domains using stratified 10-fold cross-validation, followed by statistical comparison using paired Wilcoxon signed-rank tests with Holm-Bonferroni correction. The combination of entropy and nonlinear features yielded the highest discriminative power, and the Extra Trees classifier emerged as the best model with a cross-validated F1-score of 0.902+/-0.027 and ROC AUC of 0.887+/-0.055. Final evaluation on a 20% hold-out test set confirmed robust generalization (F1= 0.922, ROC AUC = 0.941). These results highlight the potential of nonlinear and entropy signal descriptors in supporting real-time, data-driven decision-making during DBS surgeries
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