Multimodal depression detection based on an attention graph convolution and transformer

Traditional depression detection methods typically rely on single-modal data, but these approaches are limited by individual differences, noise interference, and emotional fluctuations. To address the low accuracy in single-modal depression detection and the poor fusion of multimodal features from e...

Full description

Saved in:
Bibliographic Details
Main Authors: Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He
Format: Article
Language:English
Published: AIMS Press 2025-02-01
Series:Mathematical Biosciences and Engineering
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2025024
Tags: Add Tag
No Tags, Be the first to tag this record!