AUTHOR=Guo Jiayang , Xiao Naian , Li Hailong , He Lili , Li Qiyuan , Wu Ting , He Xiaonan , Chen Peizhi , Chen Duo , Xiang Jing , Peng Xueping 

TITLE=Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.822810

DOI=10.3389/fmolb.2022.822810

ISSN=2296-889X

ABSTRACT=High frequency oscillations (HFOs), observed within 80-500 Hz of Magnetoencephalography (MEG) data, are putative biomarkers to localize epileptogenic zones that are critical for the success of surgical epilepsy treatment. It is crucial to accurately detect HFOs for improving the surgical outcome of patients with epilepsy. However, in clinical practices, detecting HFOs in MEG signals mainly depends on visual inspection by clinicians, which is very time consuming, labor-intensive, subjective, and error-prone. To accurately and automatically detect HFOs, machine learning approaches have been developed and have demonstrated the promising results of automated HFOs detection. More recently, transformer-based model has attracted wide attention and has outperformed peer models and achieved state-of-the-art performance on  many machine learning tasks. In this paper, we are investigating the suitability of transformer based models on the detection of HFOs. Specifically, We propose a transformer-based HFOs detection framework for biomedical MEG one-dimensional signal data. For signal classification, we develop a transformer-based HFOs (TransHFO) classification model. Then, we investigate the relationship between depth of deep learning models and classification performance. The experimental results show that the proposed framework outperforms the state-of-the-art HFOs classifiers,  increasing classification accuracy by 7%. Furthermore, we find that shallow TransHFO (< 10 layers) outperforms deep TransHFO models (≥ 10 layers) on most data augmented factors.