PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets

• URL: http://arxiv.org/abs/2508.07253v1
• Date: Sun, 10 Aug 2025 09:12:29 GMT
• Title: PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
• Authors: Bartlomiej Chybowski, Shima Abdullateef, Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero,
• Abstract summary: We introduce an innovative, open-source machine-learning framework that enables robust seizure detection across varied clinical datasets.<n>To enhance robustness, the framework incorporates an automated pre-processing pipeline to standardise data and a majority voting mechanism.<n>We train, tune, and evaluate models within each dataset, assessing their cross-dataset transferability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable seizure detection is critical for diagnosing and managing epilepsy, yet clinical workflows remain dependent on time-consuming manual EEG interpretation. While machine learning has shown promise, existing approaches often rely on dataset-specific optimisations, limiting their real-world applicability and reproducibility. Here, we introduce an innovative, open-source machine-learning framework that enables robust and generalisable seizure detection across varied clinical datasets. We evaluate our approach on two publicly available EEG datasets that differ in patient populations and electrode configurations. To enhance robustness, the framework incorporates an automated pre-processing pipeline to standardise data and a majority voting mechanism, in which multiple models independently assess each second of EEG before reaching a final decision. We train, tune, and evaluate models within each dataset, assessing their cross-dataset transferability. Our models achieve high within-dataset performance (AUC 0.904+/-0.059 for CHB-MIT and 0.864+/-0.060 for TUSZ) and demonstrate strong generalisation across datasets despite differences in EEG setups and populations (AUC 0.615+/-0.039 for models trained on CHB-MIT and tested on TUSZ and 0.762+/-0.175 in the reverse case) without any post-processing. Furthermore, a mild post-processing improved the within-dataset results to 0.913+/-0.064 and 0.867+/-0.058 and cross-dataset results to 0.619+/-0.036 and 0.768+/-0.172. These results underscore the potential of, and essential considerations for, deploying our framework in diverse clinical settings. By making our methodology fully reproducible, we provide a foundation for advancing clinically viable, dataset-agnostic seizure detection systems. This approach has the potential for widespread adoption, complementing rather than replacing expert interpretation, and accelerating clinical integration.

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