Epileptic spasm recognition: EEG classification using time–frequency features and machine learning
Yingtao Zhang, Jieming Li, Lin Li, Yan Chen, Jinlong Li, Wei Xing · 2025
Read Epileptic spasm recognition: EEG classification using time–frequency features and machine learning by Yingtao Zhang, Jieming Li, Lin Li, Yan Chen, Jinlong Li, Wei Xing (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12729157.txt.
Description
Yingtao Zhang and co-authors taught computer models to spot a rare seizure type in brain wave records in this 2025 paper. The seizures, called epileptic spasms, hit mostly infants and young children. They cause sudden muscle jerks and can pass out the child, and early spotting shapes treatment.
The team pulled 54 features that capture how brain signals change over time and pitch. Three standard learning models were trained on records gathered in clinics. A random forest model scored about eighty-one percent accuracy with a small feature set. A nearest-neighbor model did better as more features were added. The authors see a path to automatic watch tools, with more work needed on reliability.
Catalog record
| CREATOR | Yingtao Zhang, Jieming Li, Lin Li, Yan Chen, Jinlong Li, Wei Xing |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |