基于小波散射变换的可解释精神分裂症生物标志物发现及静息态脑电图分类
Wavelet Scattering Transform for Interpretable Schizophrenia Biomarker Discovery and Classification from Resting-State EEG
AI总结：
针对现有精神分裂症EEG分类方法存在特征缺陷、数据泄露的问题，本研究结合小波散射变换、留一受试者交叉验证与SHAP可解释性方法，实现高精度分类并识别出关键生物标志物。
摘要：
精神分裂症是一种严重的神经精神疾病，以严重的皮层网络失调为特征，目前基于脑电图的可临床转化客观生物标志物仍有待开发。现有自动分类流程主要依赖静态功率谱密度特征，这类特征无法识别作为精神分裂症病理生理学核心现象的振幅调制动态与跨频率耦合，同时其采用的时段级交叉验证策略会引发时间数据泄露，人为夸大报告的性能。本研究提出一个具备数学原理支撑的诊断框架，整合多阶小波散射变换（WST）、严格的留一受试者（LOSO）交叉验证与SHAP可解释性方法，同步实现脑电图分类与生物标志物发现。从静息态多通道脑电图中提取可捕捉多尺度振幅调制结构的分层WST系数。采用带Benjamini Hochberg错误发现率校正的受试者级方差分析识别显著生物标志物，在严格的LOSO交叉验证与受试者级多数投票规则下评估随机森林与支持向量机分类器性能。编码跨频率耦合的二阶散射系数在判别性生物标志物集中占主导，其中伽马波段特征占比最高，证明时间振幅调制是精神分裂症的主要电生理特征。P3电极被确定为单个最具判别力的位点。在严格的受试者独立评估下，随机森林取得了90.48%的分类准确率（AUC=0.9339，灵敏度=95.56%）。所提出的WST框架为基于脑电图的精神疾病生物标志物发现建立了严谨且可解释的标准，未来也可应用于精神分裂症亚型的检测。
英文摘要：
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.
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