Automated interictal epileptic spike detection from simple and noisy annotations in MEG data

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: CNN F1-score: 0.46, ANN F1-score: 0.44

• Primary Outcome: F1-score for interictal spike detection

• Nnt Or Sensitivity: Sensitivity and specificity not clearly reported; F1-scores suggest moderate diagnostic accuracy

• Confidence Interval: Not reported

Clinical Application

Requires MEG facilities and computational infrastructure; could reduce neurophysiologist workload in spike detection Relevant to major Australian epilepsy centres with MEG capabilities (Melbourne, Sydney). Could support PBS-funded presurgical evaluations by improving efficiency of MEG interpretation Patients with drug-resistant epilepsy undergoing presurgical MEG evaluation at tertiary epilepsy centres

Abstract

In drug-resistant epilepsy, presurgical evaluation can be considered for suitable candidates. Magnetoencephalography (MEG) has been shown to be an effective exam to inform the localization of the epileptogenic zone through the localization of interictal epileptic spikes. Manual detection of these pathological biomarkers remains a fastidious task due to the high dimensionality of MEG recordings, and inter-rater agreement has been reported to be only moderate. Current automated methods are unsuitable for clinical practice, either requiring extensively annotated data or lacking robustness on non-typical data. Considering a database of 82 patients, we demonstrate that deep learning models can be used for detecting interictal spikes in MEG recordings, even when only temporal and single-expert annotations are available, which represents real-world clinical practice. We propose two model architectures: a feature-based artificial neural network (ANN) and a convolutional neural network (CNN), evaluated against a state-of-the-art model to classify short time windows of signal. In addition, we employ an interactive machine learning strategy to iteratively improve our data annotation quality using intermediary model outputs. Both proposed models outperform the state-of-the-art model (F1-scores: CNN = 0.46, ANN = 0.44) when tested on 10 holdout test patients. The interactive machine learning strategy demonstrates that our models are robust to noisy annotations. Overall, results highlight the ability of our models to analyze complex and imperfectly annotated data. Our method of interactive machine learning offers great potential for faster data annotation, while our models represent useful and efficient tools for automated interictal spikes detection.

References

1. 1.Mouches, P., Jung, J., Demasson, A., Guinard, A., Bouet, R., Marchal, R., & Quentin, R. (2026). Automated interictal epileptic spike detection from simple and noisy annotations in MEG data. Brain Structure & Function. https://doi.org/10.1088/1741-2552/acef92