1. Better electrobiological markers and a improved automated diagnostic classifier for schizophrenia—based on a new EEG effective information estimation framework.
- Author
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Jing, Tianyu, Wang, Jiao, Guo, Zhifen, Ma, Fengbin, Xu, Xindong, and Fu, Longyue
- Subjects
PSYCHIATRIC diagnosis ,SIGNAL-to-noise ratio ,AUTOMATIC classification ,SIGNAL denoising ,MENTAL illness - Abstract
Advances in AI techniques have fueled research on using EEG data for psychiatric disorder diagnosis. Despite EEG's cost-effectiveness and high temporal resolution, low Signal-to-Noise Ratio (SNR) hampers critical marker extraction and model improvement, while denoising techniques will lead to a loss of effective information in EEG. The aim of this study is to employ AI methods for the processing of raw EEG data. The primary objectives of the processing are twofold: first, to acquire more reliable markers for schizophrenia, and second, to construct a superior automatic classification for schizophrenia. To remove the noises and retain task-related (classification tasks) effective information mostly, we introduce an Effective Information Estimation Framework (EIEF) based on three key principles: the task-centered approach, leveraging 1D-CNNs' test metrics to gauge effective information proportion, and feedback. We address a theoretical foundation by integrating these principles into mathematical derivations to propose the mathematical model of EIEF. In experiments, we established a paradigm pool of 66 denoising paradigms, with EIEF successfully identifying the optimal paradigms (on two datasets) for restoring effective information. Utilizing the processed dataset, we trained a 3D-CNN for automatic schizophrenia diagnosis, achieving outstanding test accuracies of 99.94 % on dataset 1 and 98.02 % on dataset 2 in subject-dependent evaluations, and accuracies of 89.85 % on dataset 1 and 98.02 % on dataset 2 in subject-independent evaluations. Additionally, we extracted 38 features from each channel of both processed and raw datasets, revealing that 20.86 % (dataset 1) of feature distribution differences between the patients and the healthy exhibited significant changes after implementing the optimal paradigm. We enhance model performance and extract more reliable electrobiological markers. These findings have promising implications for advancing the field of the clinical diagnosis and pathological analysis of Schizophrenia. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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