Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis
Zhuan Zou, Bin Chen, Dongqiong Xiao, Fajuan Tang, Xihong Li, Amaryllis Mavragani, Ali Jafarizadeh, Catherine Ordun, Seng Hansun · 2024
Read Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis by Zhuan Zou, Bin Chen, Dongqiong Xiao, Fajuan Tang, Xihong Li, Amaryllis Mavragani, Ali Jafarizadeh, Catherine Ordun, Seng Hansun (2024) 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-11669868.txt.
Description
Zhuan Zou, Bin Chen, and seven co-authors pool evidence on machine learning for spotting seizures in children. Their systematic review and meta-analysis, from twenty twenty-four, searches four major databases. Studies up to late August twenty twenty-three qualify. Quick, reliable seizure detection remains hard in pediatric wards.
Each study's bias risk is graded with a standard tool. A combined model pools accuracy across the studies. Results also break down by data type and algorithm. The synthesis gives builders of smart monitors a firmer base. Gaps in the primary studies stand out for future work. The authors aim to guide safer, better detection tools for children.
Catalog record
| CREATOR | Zhuan Zou, Bin Chen, Dongqiong Xiao, Fajuan Tang, Xihong Li, Amaryllis Mavragani, Ali Jafarizadeh, Catherine Ordun, Seng Hansun |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |