Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.
Wei-Long ZhengEdilberto AmorimJin JingOna WuMohammad GhassemiJong Woo LeeAdithya SivarajuTrudy PangSusan T. HermanNicolas GaspardBarry J. RuijterMarleen C. Tjepkema-CloostermansJeannette HofmeijerMichel J. A. M. van PuttenM. Brandon WestoverPublished in: IEEE Trans. Biomed. Eng. (2022)
Keyphrases
- deep learning
- normal subjects
- heart disease
- unsupervised learning
- acute coronary syndrome
- cardiovascular disease
- unsupervised feature learning
- brain activity
- machine learning
- epileptic seizures
- weakly supervised
- mental models
- data sets
- patient data
- coronary artery
- left ventricle
- deep architectures
- computer vision
- eeg signals
- object detection
- semi supervised
- multiscale
- data mining