2022/07/09 by Yihan Wu, Wu, Yihan, Min Xia +5
Medicine · Neuroscience · #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2207.04262
openalex publication_date 2022/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Schizophrenia (SZ) is a serious mental disorder that could seriously affect the patient's quality of life. In recent years, detection of SZ based on deep learning (DL) using electroencephalogram (EEG) has received increasing attention. In this paper, we proposed an end-to-end recurrent auto-encoder (RAE) model to detect SZ. In the RAE model, the raw data was input into one auto-encoder block, and the reconstructed data were recurrently input into the same block. The extracted code by auto-encoder block was simultaneously served as an input of a classifier block to discriminate SZ patients from healthy controls (HC). Evaluated on the dataset containing 14 SZ patients and 14 HC subjects, and the proposed method achieved an average classification accuracy of 81.81% in subject-independent experiment scenario. This study demonstrated that the structure of RAE is able to capture the differential features between SZ patients and HC subjects.