부산대학교 도서관

In clinical neurology practice, the classification of intracranial electroencephalography (iEEG) recordings into artifacts, pathological, and physiological activities has traditionally been performed by expert visual review, which is a difficult, time-consuming, and subjective process. Recently, deep learning methods have shown remarkable success in automatic iEEG recognition. However, the current convolutional neural network (CNN)-based methods for iEEG signal analysis only focus on extracting local features, while largely disregarding the global context information. To address this limitation, we propose a novel hierarchical CNN-Transformer combined network for iEEG classification, named IEEG-HCT. The proposed model first employs a CNN Stem to extract preliminary local features, which are subsequently fed into a hierarchical alternating structure comprising of convolutional embedding (CE) blocks and Transformer blocks. Our explorations of combining CNN and Transformer models are conducted at both the macro and micro levels, allowing the resulting model to effectively capture both local features and long-distance dependencies. At the macro level, we utilize CE blocks to extract local features and reduce intermediate feature size, while at the micro level, we employ appropriate convolutions to enhance Transformer blocks. In addition, the hierarchical architecture allows for the extraction of multiscale features. We evaluated the proposed IEEG-HCT model on the multicenter iEEG dataset using out-of-institution and cross-subject validations. Experimental results demonstrate that the proposed model outperforms most existing models in all experimental settings.