This work introduces a new approach to the Epileptic Spasms (ESES) detection based on the EEG signals using Vision Transformers (ViT). Classic ESES detection approaches have usually been performed with manual processing or conventional algorithms, suffering from poor sample sizes, single-channel-based analyses, and low generalization abilities. In contrast, the proposed ViT model overcomes these limitations by using the attention mechanism to focus on the important features in multi-channel EEG data, which is contributing to both better accuracy and efficiency. The model processes frequency-domain representations of EEG signals, such as spectrograms, as image data to capture long-range dependencies and complex patterns in the signal. The model demonstrates high performance with an accuracy of 97% without requiring intensive data preprocessing, thus rendering it suitable for real-time clinical applications on a large scale. The method represents a significant development in the advancement of neurological disorders such as ESES in detection and analysis.

翻译：本研究提出了一种基于脑电图（EEG）信号、利用视觉Transformer（ViT）进行癫痫性痉挛（ESES）检测的新方法。传统的ESES检测方法通常依赖于人工处理或常规算法，存在样本量小、基于单通道分析以及泛化能力差等局限性。相比之下，所提出的ViT模型通过利用注意力机制聚焦于多通道EEG数据中的重要特征，克服了这些限制，从而同时提升了检测的准确性和效率。该模型将EEG信号的频域表示（如频谱图）作为图像数据进行处理，以捕捉信号中的长程依赖关系和复杂模式。该模型表现出高性能，准确率达到97%，且无需繁重的数据预处理，因此适合大规模实时临床应用。该方法代表了在ESES等神经系统疾病的检测与分析方面的重要进展。