Epilepsy affects around 50 million people globally. Electroencephalography (EEG) or Magnetoencephalography (MEG) based spike detection plays a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training that further limits the number of qualified professionals. To ease the difficulty, various algorithmic approaches have been developed. However, the existing methods face challenges in handling varying channel configurations and in identifying the specific channels where the spikes originate. A novel Nested Deep Learning (NDL) framework is proposed 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 is shown to improve prediction accuracy, achieve channel localization, support cross-modality data integration, and adapt to various neurophysiological applications.

翻译：癫痫在全球范围内影响约五千万人。基于脑电图（EEG）或脑磁图（MEG）的棘波检测在诊断与治疗中起着至关重要的作用。人工棘波识别耗时且需要专业培训，这进一步限制了合格专业人员的数量。为缓解此困难，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以及识别棘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（NDL）框架以克服这些局限。NDL对所有通道的信号进行加权组合，确保了对不同通道设置的适应性，并使临床医生能更准确地识别关键通道。通过对真实EEG/MEG数据集的理论分析和实证验证，NDL被证明能提高预测准确性、实现通道定位、支持跨模态数据集成，并能适应各种神经生理学应用。