Critical Care Monitoring in Comatose Patients Receiving ECMO: A Cohort Study of Machine Learning on Quantitative EEG for Diagnosing Acute Brain Injury and Prognosticating Mortality
Mingfeng Cao, Jeffrey B. Wang, Beichen Shen, Zoe Soule, Kotaro Noda, Jaeho Hwang, Eva Ritzl, Yaman B. Ahmed, Hyun-Yi Woo, Siyu Wang, Tianyue Zhu, Leon Fan, Nirma Carballido Martinez, Glenn Whitman, Nitish Thakor, Sung-Min Cho · 2026
Read Critical Care Monitoring in Comatose Patients Receiving ECMO: A Cohort Study of Machine Learning on Quantitative EEG for Diagnosing Acute Brain Injury and Prognosticating Mortality by Mingfeng Cao, Jeffrey B. Wang, Beichen Shen, Zoe Soule, Kotaro Noda, Jaeho Hwang, Eva Ritzl, Yaman B. Ahmed, Hyun-Yi Woo, Siyu Wang, Tianyue Zhu, Leon Fan, Nirma Carballido Martinez, Glenn Whitman, Nitish Thakor, Sung-Min Cho (2026) 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-13566830.txt.
Description
Mingfeng Cao and co-authors published a 2026 study of machine learning on brainwave data to flag brain injury in patients on ECMO, a heart-lung support machine. Brain injury is a leading killer in these patients, and scans are hard to run on the critically ill. The team studied 57 comatose adults who had continuous brainwave monitoring during a standard pause in sedation. Numbers drawn from the brainwaves, plus clinical data, trained several classifiers.
Brain injury struck 37 percent of the patients, mostly from stroke. Models using brainwave features beat models using clinical data alone at spotting injury, though the gap was not firm. Two features, frontal theta power and lopsidedness between brain halves, mattered most. Brainwave features also predicted 30-day death well. The authors propose the method as a bedside early warning that can rush the right patients to scans.
Catalog record
| CREATOR | Mingfeng Cao, Jeffrey B. Wang, Beichen Shen, Zoe Soule, Kotaro Noda, Jaeho Hwang, Eva Ritzl, Yaman B. Ahmed, Hyun-Yi Woo, Siyu Wang, Tianyue Zhu, Leon Fan, Nirma Carballido Martinez, Glenn Whitman, Nitish Thakor, Sung-Min Cho |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 31 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |