Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage

This dataset supports our study on developing an automated algorithm for
predicting delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH)
using electroencephalography (EEG). We provide EEG recordings and clinical
data from 113 moderate to severe grade SAH patients, along with the code for
our machine learning model that integrates multiple EEG features for DCI
prediction. The algorithm achieves an area under the receiver-operator curve
of 0.73 by day 5 after SAH with good calibration between 48-72 hours,
demonstrating the potential of automated, multi-featured EEG assessment for
DCI risk prediction.

The citation is:

[Zheng WL, Kim JA, Elmer J, Zafar SF, Ghanta M, Moura Junior V, Patel A,
Rosenthal E, Brandon Westover M. Automated EEG-based prediction of delayed
cerebral ischemia after subarachnoid hemorrhage. Clin Neurophysiol. 2022
Nov;143:97-106. doi: 10.1016/j.clinph.2022.08.023. Epub 2022 Sep 11. PMID:
36182752; PMCID:
PMC9847346.](https://pmc.ncbi.nlm.nih.gov/articles/PMC9847346/)

Code is here: <https://github.com/bdsp-core/SAH_DCI_Prediction_EEG>