MDEFusion: A Multi‐Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half‐Split Crossover SAE for Schizophrenia Recognition | Synapse
April 25, 2026Open Access
MDEFusion: A Multi‐Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half‐Split Crossover SAE for Schizophrenia Recognition
Key Points
This research aims to develop an EEG analysis tool for the auxiliary diagnosis of schizophrenia.
Developed a multi-domain EEG feature fusion network.
Utilized bidirectional attention LSTM and half-split crossover SAE.
Assessed clinical decision support effectiveness in schizophrenia recognition.
Demonstrated significant accuracy in recognizing schizophrenia through EEG analysis.
Enhanced reliability of clinical decision-making processes for schizophrenia diagnosis.
Abstract
This paper provides an efficient and reliable EEG analysis tool for the auxiliary diagnosis of schizophrenia, demonstrating significant application value for clinical decision support systems.