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

TL;DR

This paper presents deep learning models capable of detecting interictal epileptic spikes in MEG data using minimal and noisy annotations, improving robustness and efficiency in clinical settings.

Contribution

It introduces two simple neural network architectures and an interactive annotation strategy that outperform existing models on noisy, real-world MEG data.

Findings

Deep learning models outperform state-of-the-art in noisy conditions

Models are robust with minimal expert annotations

Interactive strategy improves annotation quality

Abstract

In drug-resistant epilepsy, presurgical evaluation of epilepsy can be considered. 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 and error-prone task due to the high dimensionality of MEG recordings, and interrater 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. In this work, 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), trained on a database of 59 patients, and 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 robustness of models with simple architectures when analyzing 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.