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.

翻译：目标：寻找感兴趣的事件是生物医学信号处理中的常见任务，癫痫发作检测与信号伪迹识别是两个关键实例。基于时段的分类是检测此类信号事件的典型机器学习框架，因其能直接应用经典机器学习技术。然而，通常需要后处理以实现良好性能并强制时间依赖性。设计合适的后处理方案以将这些分类输出转换为事件，是该框架中繁琐且劳动密集的环节。方法：我们提出一个直接以事件为学习目标的事件驱动建模框架，摒弃了将模型输出转换为事件的临时后处理方案。通过模拟数据和真实世界数据，我们展示了该框架的实用性，并与基于时段的建模方法进行了比较。结果：我们证明，无需后处理的事件驱动建模在性能上可达到或超越需经过繁重后处理的时段建模。结论：这些结果表明，将事件直接作为学习目标（而非通过临时后处理获取）的强大优势，显著减少了设计工作量。意义：该事件驱动建模框架可轻松应用于信号处理中的其他事件检测问题，无需针对特定任务进行大量后处理。