用小波散射变换挖掘脑电特征，精准识别精神分裂症生物标志物。
Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
- 通过多阶小波散射变换捕捉脑电信号的多尺度调幅结构。
- 在严格个体独立验证下达到90.48%准确率，Gamma频段特征最显著。
- 结果可解释，适合精神分裂症分型研究与临床转化应用。
精神分裂症是一种由皮层网络失调引起的严重神经精神障碍，目前基于脑电图（EEG）的客观生物标志物仍不充分。现有自动分类方法主要依赖静态功率谱密度特征，忽视了幅度调制动态和跨频耦合现象，且采用逐段交叉验证策略引入时间数据泄露，人为夸大性能。本研究提出一种数学严谨的诊断框架，结合多阶小波散射变换（WST）、严格的留一被试外（LOSO）交叉验证及SHAP可解释性分析，实现脑电分类与生物标志物发现同步进行。从静息态多通道脑电中提取层次化小波散射系数，利用带贝尼蒂-霍赫伯格校正的主体级方差分析识别显著生物标志物。随机森林与支持向量机在严格LOSO验证和主体级多数投票下评估性能。二阶散射系数编码的跨频耦合主导判别特征集，以伽马频段特征最为普遍，表明时间幅度调制是精神分裂症的主要电生理特征。电极P3为最具判别力位置。在严格个体独立评估下，随机森林达90.48%准确率（AUC = 0.9339；敏感度 = 95.56%）。所提WST框架为脑电驱动的精神病生物标志物发现建立了严谨、可解释的标准，未来亦可用于精神分裂症亚型检测。
原文摘要 · Abstract (English)
Schizophrenia is a debilitating neuropsychiatric disorder characterized by profound cortical network dysregulation, for which objective, clinically translatable EEG based biomarkers remain underdeveloped. Existing automated classification pipelines rely predominantly on static power spectral density features inherently blind to amplitude modulation dynamics and cross-frequency coupling, phenomena central to schizophrenia pathophysiology, while adopting epoch level cross validation strategies that introduce temporal data leakage, artificially inflate reported performance. This study introduces a mathematically principled diagnostic framework integrating the multi-order Wavelet Scattering Transform(WST), strict Leave One Subject Out (LOSO) cross-validation, and SHAP explainability for simultaneous EEG classification and biomarker discovery. Hierarchical WST coefficients capturing multi-scale amplitude modulation structure were extracted from resting state multichannel EEG. Subject-level ANOVA with Benjamini Hochberg false discovery rate correction identified significant biomarkers, with Random Forest and SVM classifiers evaluated under strict LOSO cross validation and subject-level majority voting. Second-order scattering coefficients encoding cross frequency coupling dominated the discriminative biomarker set, with gamma-band features most prevalent, demonstrating that temporal amplitude modulation constitutes the primary electrophysiological signature of schizophrenia. Electrode P3 was identified as the single most discriminative site. Under rigorous subject independent evaluation, the Random Forest achieved 90.48% accuracy (AUC = 0.9339; sensitivity = 95.56%). The proposed WST framework establishes a rigorous, interpretable standard for EEG-driven psychiatric biomarker discovery that can also be applicable in the detection of schizophrenia subtypes in the future.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。