Building an auxiliary diagnostic and treatment efficacy prediction model for adolescent depression using machine learning based on electroencephalography technology
Ting Peng, Aiping Chi, Jinghui Yang, Yizhong Ren, Yanru Li, Jing Fan · 2026
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Description
Ting Peng and colleagues use brain-wave data to build tools that may help spot teen depression and pick who gains from exercise. The 2026 study tapes six minutes of resting brain waves. Teens in the study were healthy or had depression. Talk-based scales rule care now, and no firm body sign shows who will gain from exercise.
The team split the brain into zones and nets, then drew out link traits in three wave bands. A three-step screen kept the best traits. Four model types were trained and cross-checked for two jobs. Job one split sick teens from well teens. Job two sorted sick teens by how much a virtual exercise plan helped them. The aim is care matched to each teen.
Catalog record
| CREATOR | Ting Peng, Aiping Chi, Jinghui Yang, Yizhong Ren, Yanru Li, Jing Fan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 108 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |