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

Abstract

Objective: 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. Methods: 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. Results: 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). Significance: 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.

From basic sciences and engineering to epileptology: A translational approach

“…Recent investigations explored the use of quantitative scalp EEG analysis to assist the diagnosis of epilepsy, mainly based on the use of ML. [54][55][56][57][58][59] For example, SpikeNet, a deep neural network, was trained on a total of 9571 scalp EEG records (with and without spikes) to perform spike detection and showed performances compared to those achieved by fellowship-trained neurophysiology experts. 54 On the other side, DeepSpike was developed for the detection of epileptiform discharges based on multiple instance object detection and required a relatively low number of labeled training data.…”

“…55 For a review regarding the use of DL for the detection of epileptiform discharges from scalp EEG, readers are referred to Ref. 59 In line with these investigations, Nadalin et al (2021) trained a CNN for spike ripple detection based on recordings from a total of 34 subjects. 60 Matos et al (2022) proposed a classifier for supporting the diagnosis of epilepsy, based on functional connectivity features of EEG in patients who had a first seizure.…”

“…Recent investigations explored the use of quantitative scalp EEG analysis to assist the diagnosis of epilepsy, mainly based on the use of ML 54–59 . For example, SpikeNet, a deep neural network, was trained on a total of 9571 scalp EEG records (with and without spikes) to perform spike detection and showed performances compared to those achieved by fellowship‐trained neurophysiology experts 54 .…”

“…On the other side, DeepSpike was developed for the detection of epileptiform discharges based on multiple instance object detection and required a relatively low number of labeled training data 55 . For a review regarding the use of DL for the detection of epileptiform discharges from scalp EEG, readers are referred to Ref 59. In line with these investigations, Nadalin et al (2021) trained a CNN for spike ripple detection based on recordings from a total of 34 subjects 60 .…”

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

“…However, a short-term interval electroencephalogram (EEG) cannot accurately record the IEDs; therefore, the diagnosis of PWEs is based on video electroencephalogram (VEEG) in clinical practice. When interictal, electrical activity in the brains of PWEs remains relatively normal, usually without epileptiform discharges, which adds difficulty to the diagnosis of epilepsy [ 4 ]. Moreover, long-term EEG is relatively expensive, and the required labor is relatively high.…”