# SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms

arXiv: https://arxiv.org/abs/2402.13005 · HTML: https://mlsanity.com/paper/2402.13005/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices](https://mlsanity.com/paper/2106.08008/) (2021)
- [Importance of methodological choices in data manipulation for validating epileptic seizure detection models](https://mlsanity.com/paper/2302.10672/) (2023)
- [Hierarchical Event Descriptor library schema for EEG data annotation](https://mlsanity.com/paper/2310.15173/) (2023)

## Built on by (most cited first, 16 of 16)

- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications](https://mlsanity.com/paper/2507.09882/) (2025)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework](https://mlsanity.com/paper/2507.08874/) (2025)
- [MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots](https://mlsanity.com/paper/2408.01988/) (2024)
- [Large EEG-U-Transformer for Time-Step Level Detection Without Pre-Training](https://mlsanity.com/paper/2504.00336/) (2025)
- [How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance](https://mlsanity.com/paper/2406.01529/) (2024)
- [MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection](https://mlsanity.com/paper/2503.08251/) (2025)
- [Adaptive Segmentation of EEG for Machine Learning Applications](https://mlsanity.com/paper/2508.20336/) (2025)
- [Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection](https://mlsanity.com/paper/2509.13974/) (2025)
- [SeFEF: A Seizure Forecasting Evaluation Framework](https://mlsanity.com/paper/2510.11275/) (2025)
- [Adapting Tensor Kernel Machines to Enable Efficient Transfer Learning for Seizure Detection](https://mlsanity.com/paper/2512.02626/) (2025)
- [LookAroundNet: Extending Temporal Context with Transformers for Clinically Viable EEG Seizure Detection](https://mlsanity.com/paper/2601.06016/) (2026)
- [Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU](https://mlsanity.com/paper/2606.20074/) (2026)
- [From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks](https://mlsanity.com/paper/2606.22258/) (2026)
- [RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models](https://mlsanity.com/paper/2609.04007/) (2026)

## Abstract (arXiv)

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 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.