Automated spectrographic seizure detection using convolutional neural networks

Abstract

Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many intensive care units employ continuous EEG (cEEG) for seizure monitoring. Although cEEG is continuously recorded, it is often reviewed intermittently, which may delay seizure diagnosis and treatment. This may be mitigated with automated seizure detection. In this study, we develop and evaluate convolutional neural networks (CNN) to automate seizure detection on EEG spectrograms. Methods: Adult EEGs (12 patients, 12 EEGs, 33 seizures) from New-York Presbyterian Hospital (NYP) and pediatric EEGs (22 patients, 130 EEGs, 177 seizures) from Children's Hospital Boston (CHB) were converted into spectrograms. To simulate a telemetry display, seizure and non-seizure events on spectrograms were sequentially sampled as images across a detection window (26,380 total images). Four CNN models of increasing complexity (number of layers) were trained, cross-validated, and tested on CHB and NYP spectrographic images. All CNNs were based on the VGG-net architecture, with adjustments to alleviate overfitting. Results: For spectrographically visible seizures, two CNN models (containing 4 and 7 convolution layers) achieved > 90% seizure detection sensitivity and specificity on the CHB test set and > 90% sensitivity and 75-80% specificity on the NYP test set. The one CNN model (10 convolution layers) did not converge during training; while another CNN (2 convolution layers) performed poorly (60% sensitivity and 32% specificity) on the NYP test set. Conclusions: Seizure detection on EEG spectrograms with CNN models is feasible with sensitivity and specificity potentially suitable for clinical use.

Deep learning-based electroencephalography analysis: a systematic review

“…Our search methodology returned 49 journal papers, 58 conference and workshop papers, 48 preprints and 1 journal paper supplement ( [201], included in the "Journal" category in our analysis) that met our criteria. A total of 23 journal and conference papers had initially been made available as preprints on arXiv or bioRxiv.…”

Accurate detection of spontaneous seizures using a generalized linear model with external validation

“…We anticipate that performance of the extrapolated classifier, in particular, may be further improved by inclusion of additional animals to fully exemplify focal seizure patterns. Data sets with hundreds of subjects consistently train classifiers with high performance for both pooled and extrapolated classifiers 11,21,[33][34][35] using both human and rodent EEG data. We observed lower performance for the GLM in the single-channel Multifocal Epilepsy data set (AUROC of 0.963), which we ascribe to less stereotyped seizure patterns than those found in unilateral focal seizures.…”

“…1 Several seizure detection methods use a single feature type, such as a power spectrogram, as feature input. [9][10][11] In our approach, we utilize twenty distinct feature classes in the time and frequency domains. Basic single-channel features such as root mean square and coastline computed in the time domain are broadly descriptive of seizures and other hyperexcitation events.…”

“…Several seizure detection methods use a single feature type, such as a power spectrogram, as feature input 9–11 . In our approach, we utilize twenty distinct feature classes in the time and frequency domains.…”

The Survey of Image Generation from EEG Signals based on Deep Learning

“…Comparing with image generation techniques from the EEG signals, these non-visual applications have initially applied in real life practice. In clinical applications, seizure detection algorithms using CNN or RNN are proposed in [27] and [28], which are the successful performance of the EEG signals' application. The above methods act on no more than ten categories classifying and most of them can only apply to single BCI paradigm.…”