Deep learning for automated epileptiform discharge detection from scalp EEG: a systematic review.

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Abstract

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from scalp EEG and establish recommendations for the clinical research community.We conduct a systematic review according to the PRISMA guidelines. We searched for studies published between 2012 and 2021 implementing DL for automating IED detection from scalp EEG in major medical and engineering databases. We highlight trends and formulate recommendations for the research community by analyzing various aspects: data properties, preprocessing methods, DL architectures, evaluation metrics and results, and reproducibility.The search yielded 67 studies, and 17 met our inclusion criteria. There were 2 main DL networks, convolutional neural networks in 14 studies and the long short-term memory networks in 3 studies. A hybrid approach combining a hidden Markov model with an autoencoder was employed in 1 study. All DL models involved supervised learning. The median number of layers was 11 (IQR: 5 – 19). The median number of IEDs was 10,122 (IQR: 2,757 – 16,824). Only 6 studies acquired data from multiple clinical centers. AUC was the most reported metric (median: 0.94; IQR: 0.92 – 0.96).The application of DL to IED detection is still limited and lacks standardization in data collection, multi-center testing, and reporting of clinically relevant metrics (i.e., F1, AUCPR, and false-positive/minute). However, the performance is promising, suggesting that DL might be a helpful approach. Further testing on multiple datasets from different clinical centers is required to confirm the generalizability of these methods.© 2022 IOP Publishing Ltd.