RT Journal Article SR Electronic T1 Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion JF bioRxiv FD Cold Spring Harbor Laboratory SP 2024.09.01.610707 DO 10.1101/2024.09.01.610707 A1 Chen, Shimiao A1 Huang, Dong A1 Liu, Xinyue A1 Chen, Jianjun A1 Kong, Xiangzeng A1 Zhang, Tingting YR 2024 UL http://biorxiv.org/content/early/2024/09/03/2024.09.01.610707.abstract AB Electroencephalography (EEG) serves as a practical auxiliary tool deployed to diagnose diverse brain-related disorders owing to its exceptional temporal resolution, non-invasive characteristics, and cost-effectiveness. In recent years, with the advancement of machine learning, automated EEG pathology diagnostics methods have flourished. However, most existing methods usually neglect the crucial spatial correlations in multi-channel EEG signals and the potential complementary information among different domain features, both of which are keys to improving discrimination performance. In addition, latent redundant and irrelevant features may cause overfitting, increased model complexity, and other issues. In response, we propose a novel feature-based framework designed to improve the diagnostic accuracy of multi-channel EEG pathology. This framework first applies a multi-resolution decomposition technique and a statistical feature extractor to construct a salient time-frequency feature space. Then, spatial distribution information is channel-wise extracted from this space to fuse with time-frequency features, thereby leveraging their complementarity to the fullest extent. Furthermore, to eliminate the redundancy and irrelevancy, a two-step dimension reduction strategy, including a lightweight multi-view time-frequency feature aggregation and a non-parametric statistical significance analysis, is devised to pick out the features with stronger discriminative ability. Comprehensive examinations of the Temple University Hospital Abnormal EEG Corpus V. 2.0.0 demonstrate that our proposal outperforms state-of-the-art methods, highlighting its significant potential in clinically automated EEG abnormality detection.Competing Interest StatementThe authors have declared no competing interest.