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+---
+# Description of a seizure detection algorithm
+title: "Seeuws - Avoiding Post-Processing With Event-Based Detection in Biomedical Signals" 
+image: "ghcr.io/esl-epfl/eventnet_2024:latest"
+authors:
+  - given-names: Nick
+    family-names: Seeuws
+    affiliation: KU Leuven
+  - given-names: Clément
+    family-names: Samanos
+    affiliation: EPFL-ESL
+  - given-names: Jonathan
+    family-names: Dan
+    email: jonathan.dan@epfl.ch
+    affiliation: EPFL-ESL
+    orcid: 'https://orcid.org/0000-0002-2338-572X'
+version: 0.1
+date-released: "2024-03-11"
+abstract: >
+  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 tailored 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.
+  This model was trained on the train and dev set of TUSZ v2.0.3.
+license: GPL-3.0
+repository: https://github.com/esl-epfl/eventnet_2024
+
+# List all datasets that were used to train this algorithm
+Dataset:
+    - title: "TUH EEG Seizure Corpus v2.0.3"
+      license: "https://isip.piconepress.com/projects/nedc/forms/tuh_eeg.pdf"
+      identifiers:
+        - description: >
+            This database is a subset of the TUH EEG Corpus that was collected 
+            from archival records of clinical EEG at Temple University Hospital 
+            recorded between 2002 – 2017. From this large dataset, a subset of 
+            files with a high likelihood of containing seizures was retained 
+            based on clinical notes and on the output of seizure detection 
+            algorithms.
+            V2.0.0 contains 7377 .edf files from 675 subjects for a total 
+            duration of 1476 hours of data. The files are mostly short 
+            (avg. 10 minutes). The dataset has heterogeneous sampling frequency 
+            and number of channels. All files are acquired at a minimum of 250 
+            Hz. A minimum of 17 EEG channels is available in all recordings. 
+            They are positioned according to the 10-20 system.
+            The annotations are provided as .csv and contain the start time, 
+            stop, channel and seizure type.
+          type: url
+          value: "https://isip.piconepress.com/projects/nedc/html/tuh_eeg/"
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