  Objective: Finding events of interest is a common task in biomedical signal
processing. The detection of epileptic seizures and signal artefacts are two
key examples. Epoch-based classification is the typical machine learning
framework to detect such signal events because of the straightforward
application of classical machine learning techniques. Usually, post-processing
is required to achieve good performance and enforce temporal dependencies.
Designing the right post-processing scheme to convert these classification
outputs into events is a tedious, and labor-intensive element of this
framework. Methods: We propose an event-based modeling framework that directly
works with events as learning targets, stepping away from ad-hoc
post-processing schemes to turn model outputs into events. We illustrate the
practical power of this framework on simulated data and real-world data,
comparing it to epoch-based modeling approaches. Results: We show that
event-based modeling (without post-processing) performs on par with or better
than epoch-based modeling with extensive post-processing. Conclusion: These
results show the power of treating events as direct learning targets, instead
of using ad-hoc post-processing to obtain them, severely reducing design
effort. Significance: The event-based modeling framework can easily be applied
to other event detection problems in signal processing, removing the need for
intensive task-specific post-processing.
