# Avoiding Post-Processing with Event-Based Detection in Biomedical Signals

arXiv: https://arxiv.org/abs/2209.11007 · HTML: https://mlsanity.com/paper/2209.11007/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Training-Time-Friendly Network for Real-Time Object Detection](https://mlsanity.com/paper/1909.00700/) (2019)
- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal](https://mlsanity.com/paper/1812.04079/) (2018)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)

## Built on by (most cited first, 5 of 5)

- [Large EEG-U-Transformer for Time-Step Level Detection Without Pre-Training](https://mlsanity.com/paper/2504.00336/) (2025)
- [Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms](https://mlsanity.com/paper/2504.08469/) (2025)
- [LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection](https://mlsanity.com/paper/2601.06016/) (2026)
- [DANCE: Detect and Classify Events in EEG](https://mlsanity.com/paper/2605.10688/) (2026)
- [NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detection](https://mlsanity.com/paper/2610.20349/) (2026)

## Abstract (arXiv)

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.