Epilepsy affects over 50 million people globally, with EEG/MEG-based spike detection playing a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training, limiting the number of professionals available to analyze EEG/MEG data. To address this, various algorithmic approaches have been developed. However, current methods face challenges in handling varying channel configurations and in identifying the specific channels where spikes originate. This paper introduces a novel Nested Deep Learning (NDL) framework designed to overcome these limitations. NDL applies a weighted combination of signals across all channels, ensuring adaptability to different channel setups, and allows clinicians to identify key channels more accurately. Through theoretical analysis and empirical validation on real EEG/MEG datasets, NDL demonstrates superior accuracy in spike detection and channel localization compared to traditional methods. The results show that NDL improves prediction accuracy, supports cross-modality data integration, and can be fine-tuned for various neurophysiological applications.

翻译：癫痫影响全球超过5000万人，基于脑电图/脑磁图的尖峰波检测在诊断和治疗中起着关键作用。手动尖峰波识别耗时且需要专业训练，限制了能够分析脑电图/脑磁图数据的专业人员数量。为解决这一问题，已开发出多种算法方法。然而，现有方法在处理变化的通道配置及识别尖峰波起源的具体通道方面面临挑战。本文提出了一种新颖的嵌套深度学习框架，旨在克服这些限制。NDL通过对所有通道信号进行加权组合，确保了对不同通道设置的适应性，并使临床医生能够更准确地识别关键通道。通过对真实脑电图/脑磁图数据集的理论分析和实证验证，NDL在尖峰波检测和通道定位方面展现出优于传统方法的准确性。结果表明，NDL提高了预测精度，支持跨模态数据整合，并可针对各种神经生理学应用进行微调。