癫痫中的机器学习

摘要(中文译文)

癫痫是一种复杂的神经系统疾病，其特征是病理过程在多个生物学尺度上展开，从细胞兴奋性和突触整合，到脑电图（EEG）记录中可观察到的大规模网络动力学。尽管传统分析方法提供了有价值的见解，但它们往往无法捕捉当代电生理和临床数据集的高维和非线性结构。因此，机器学习（ML）已成为癫痫研究中强大的分析框架，尽管其快速应用揭示了算法性能与生物学可解释性之间日益扩大的差距。本综述考察了在三个分析上不同但相互关联的层面上运作的ML方法：（i）利用高维电生理数据进行细胞层面表型分析的无监督学习；（ii）用于基于EEG的癫痫发作检测和预测的监督学习；（iii）整合神经元和网络动力学的多尺度建模框架。我们并非提供详尽的算法目录，而是聚焦于ML应用背后的推断假设、限制泛化性和临床相关性的方法学缺陷，以及ML衍生的表征如何在已建立的神经生理学理论中加以解释。我们强调，无监督ML有助于识别传统单变量分析中掩盖的潜在兴奋性表型和轨迹，而监督ML在自动癫痫发作检测和预测方面取得了实质性进展，尽管在数据泄漏、类别不平衡和模糊的发作前期标注方面仍存在持续挑战。我们认为，最有前景的方向在于将ML嵌入多尺度机制模型，其中数据驱动的推断促进参数估计和假设生成，而非黑箱预测。通过优先考虑可解释性、严格验证和跨尺度整合，ML增强的多尺度框架为通向临床可操作的癫痫模型提供了路径。

Abstract

Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excitability and synaptic integration to large-scale network dynamics observable in electroencephalographic (EEG) recordings. While traditional analytical approaches have provided valuable insights, they often fail to capture high-dimensional and nonlinear structure of contemporary electrophysiological and clinical datasets. Consequently, machine learning (ML) has emerged as a powerful analytical framework in epilepsy research, although its rapid adoption has revealed a growing gap between algorithmic performance and biological interpretability. This review examines ML methods operating across three analytically distinct yet interconnected levels: (i) unsupervised learning for cellular-level phenotyping using high-dimensional electrophysiological data; (ii) supervised learning for EEG-based seizure detection and prediction; and (iii) multiscale modeling frameworks integrating neuronal and network dynamics. Rather than providing an exhaustive catalog of algorithms, we focus on inferential assumptions underlying ML applications, the methodological pitfalls constraining generalization and clinical relevance, and how ML-derived representations can be interpreted within established neurophysiological theory. We highlight that unsupervised ML facilitates identification of latent excitability phenotypes and trajectories obscured in traditional univariate analyses, while supervised ML has substantially advanced automated seizure detection and prediction, despite persistent challenges related to data leakage, class imbalance, and ambiguous preictal labeling. We argue that the most promising direction lies in embedding ML within multiscale mechanistic models, where data-driven inference facilitates parameter estimation and hypothesis generation rather than black-box prediction. By prioritizing interpretability, rigorous validation, and cross-scale integration, ML-enhanced multiscale frameworks offer a path toward clinically actionable models of epilepsy.

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Epilepsy research 的其他文献

• 灰质体积与癫痫临床结果的关系：一项区域队列研究