Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

TL;DR

This study demonstrates that convolutional neural networks significantly improve the automatic classification of EEG recordings as pathological or normal, providing interpretable visualizations and a new benchmark for clinical EEG analysis.

Contribution

The paper introduces optimized shallow and deep ConvNet architectures that outperform previous methods in EEG pathology detection and offers insights into their decision-making process through visualization.

Findings

ConvNets achieved ~85% accuracy, outperforming previous results (~79%)

Spectral power in delta and theta bands was key to classification

Automated hyperparameter optimization revealed novel architectures

Abstract

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.