Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG.

ABSTRACT:

Background

Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment.

Methods

In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders.

Results

Psychiatric disorders were identified u

SUBMITTER: Yan W 

PROVIDER: S-EPMC10333510 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature