Measurement of psychological stress in nursing staff based on BiLSTM+Attention analysis of EEG signals
10.3969/j.issn.1005-202X.2025.05.015
- VernacularTitle:基于BiLSTM+Attention分析脑电信号测量临床护理人员心理压力
- Author:
Enjiang ZHU
1
;
Ming LI
;
Jianzhi SUN
;
Xiaoming CHEN
;
Wenwen MENG
;
Xia XING
Author Information
1. 北京工商大学计算机学院,北京 100048
- Publication Type:Journal Article
- Keywords:
psychological stress;
measurement and assessment;
electroencephalography signal;
deep learning,bidirectional long short-term memory model;
attention mechanism
- From:
Chinese Journal of Medical Physics
2025;42(5):651-659
- CountryChina
- Language:Chinese
-
Abstract:
As a non-invasive physiological indicator,electroencephalography signal provides an objective assessment of psychological stress levels among nursing staff in major public health emergencies,offering a scientific basis for targeted psychological interventions while overcoming the limitations of subjective bias inherent in traditional questionnaire-based methods.A psychological stress classification model based on bidirectional long short-term memory and attention mechanism is proposed to classify the psychological stress of clinical nurses more effectively by analyzing their electroencephalography signals.Experimental results show that the proposed model exhibits better classification performance than the traditional long short-term memory model on the DREAMER dataset,the Feeling Emotions dataset and the self-built dataset.This study provides a novel approach for assessing psychological stress,which is helpful to improve the pertinence and effectiveness of clinical nursing work.
