SzCORE : Seizure Community Open‐Source Research Evaluation framework for the validation of electroencephalography ‐based automated seizure detection algorithms

The need for high‐quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long‐term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comp...

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Veröffentlicht in:Epilepsia (Copenhagen) Jg. 66; H. S3; S. 14 - 24
Hauptverfasser: Dan, Jonathan, Pale, Una, Amirshahi, Alireza, Cappelletti, William, Ingolfsson, Thorir Mar, Wang, Xiaying, Cossettini, Andrea, Bernini, Adriano, Benini, Luca, Beniczky, Sándor, Atienza, David, Ryvlin, Philippe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.09.2025
Schlagworte:
Algorithms
Business metrics
Convulsions & seizures
Datasets
Detection Algorithms
EEG
Electroencephalography
Electroencephalography - methods
Electroencephalography - standards
Epilepsy
Humans
Learning algorithms
Machine Learning
Seizures
Seizures - diagnosis
Seizures - physiopathology
electroencephalography
machine‐learning benchmark
brain imaging data structure
seizure detection algorithms
ISSN:0013-9580, 1528-1167, 1528-1167
Online-Zugang:Volltext
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Zusammenfassung:The need for high‐quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long‐term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG‐based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross‐validation strategies, and performance metrics. We also propose the EEG 10–20 seizure detection benchmark, a machine‐learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine‐learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open‐Source Research Evaluation) framework and benchmark are made publicly available along with an open‐source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.
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ISSN:0013-9580
1528-1167
1528-1167
DOI:10.1111/epi.18113