Advance in research on EEG technology in non-suicidal self-injurious behaviour and suicidal ideation in de-pression
10.3969/j.issn.1002-0152.2024.09.008
- VernacularTitle:脑电技术在抑郁症非自杀性自伤行为和自杀意念中研究进展
- Author:
Yuhang HE
1
;
Yun YI
;
Hehua LI
;
Fengchun WU
;
Fan JIANG
;
Kai WU
;
Yuanyuan HUANG
Author Information
1. 广州医科大学附属脑科医院精神科(广州 510370)
- Keywords:
Depression;
Self-injurious behaviour;
Suicidal ideation;
Suicide attempt;
EEG;
Event-related po-tentials;
Machine learning
- From:
Chinese Journal of Nervous and Mental Diseases
2024;50(9):560-564
- CountryChina
- Language:Chinese
-
Abstract:
Non-suicidal self-injury behavior and suicidal ideation are important risk factors for suicide attempts in patients with depression.At present,studies have found that there are obvious differences in electroencephalogram(EEG)characteristics between these two.Resting-state EEG analysis reveals that the absolute power of Gamma in specific electrode points is significantly increased in patients with depression accompanied by suicidal ideation.The Beta and Gamma activities of those with non-suicidal self-injury behavior changed.EEG microstate research shows that there are differences in microstates between those with non-suicidal self-injury behavior and those with suicidal ideation.Among them,short segments of microstate sequences have certain potential.In event-related potentials,these two show specific characteristics in components such as P3 wave and N2 wave.The amplitude of N2 wave in patients with depression accompanied by non-suicidal self-injury behavior is lower.Although there are few machine learning-related studies on EEG technology,it has shown certain application prospects.Therefore,this article will summarize the progress of EEG research related to the two,aiming to provide a basis for early prediction of suicide in depression.
