EventNet: Detecting Events in EEG

Neurologists are often looking for various "events of interest" when analyzing EEG. To support them in this task various machine-learning-based algorithms have been developed. Most of these algorithms treat the problem as classification, thereby independently processing signal segments and ignoring temporal dependencies inherent to events of varying duration. At inference time, the predicted labels for each segment then have to be post processed to detect the actual events. We propose an end-to-end event detection approach (EventNet), based on deep learning, that directly works with events as learning targets, stepping away from ad-hoc postprocessing schemes to turn model outputs into events. We compare EventNet with a state-of-the-art approach for artefact and and epileptic seizure detection, two event types with highly variable durations. EventNet shows improved performance in detecting both event types. These results show the power of treating events as direct learning targets, instead of using ad-hoc postprocessing to obtain them. Our event detection framework can easily be extended to other event detection problems in signal processing, since the deep learning backbone does not depend on any task-specific features.