On the road to electroencephalography-based prehospital detection of large vessel occlusion stroke

Endovascular thrombectomy (EVT) is standard treatment for patients with anterior large vessel occlusion (LVO-a) stroke. Prehospital detection of patients with LVO-a stroke would enable direct routing of these patients to an EVT-capable center, thereby reducing time-to-treatment and improving patient outcome. An effective method for prehospital detection of LVO-a stroke must meet the following requirements: high diagnostic accuracy, fast application and interpretation, user-friendly, compact, and relatively affordable. However, current methods do not fulfill all these requirements. In this thesis, we took the first steps on the road to prehospital detection of LVO-a stroke based on electroencephalography (EEG). We performed dry electrode EEG recordings in patients with a suspected stroke in the emergency room and in the prehospital setting. The results indicate that EEG has the potential to detect LVO-a stroke among patients with suspected stroke in the prehospital setting, but EEG data quality needs to be improved before future implementation in prehospital stroke care. An alternative EEG setup which is potentially fast and easy in application and yields high data quality is subhairline EEG. We performed subhairline EEG recordings in patients with suspected stroke in the emergency room. Subhairline EEG detected LVO-a stroke with high diagnostic accuracy and had high data reliability, but validation in the prehospital setting is necessary.