Yutang Li, Dezhi Cao, Junda Qu, Wei Wang, Xinhui Xu, Lingyu Kong, Jianxiang Liao, Wenhan Hu, Kai Zhang, Jihan Wang, Chunlin Li, Xiaofeng Yang, Xu Zhang
OBJECTIVE: Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. METHODS: An initial detection module was designed to extract candidate high-frequency oscillations...
April 16, 2024: IEEE Transactions on Neural Systems and Rehabilitation Engineering