Unsupervised seizure identification on EEG

Abstract

Background and objective: Epilepsy is one of the most common neurological disorders, whose development is typically detected via early seizures. Electroencephalogram (EEG) is prevalently employed for seizure identification due to its routine and low expense collection. The stochastic nature of EEG makes manual seizure inspections laborsome, motivating automated seizure identification. The relevant literature focuses mostly on supervised machine learning. Despite their success, supervised methods require expert labels indicating seizure segments, which are difficult to obtain on clinically-acquired EEG. Thus, we aim to devise an unsupervised method for seizure identification on EEG.

Methods: We propose the first fully-unsupervised deep learning method for seizure identification on raw EEG, using a variational autoencoder (VAE). In doing so, we train the VAE on recordings without seizures. As training captures non-seizure activity, we identify seizures with respect to the reconstruction errors at inference time. Moreover, we extend the traditional VAE training loss to suppress EEG artifacts. Our method does not require ground-truth expert labels indicating seizure segments or manual feature extraction.

Results: We implement our method using the PyTorch library and execute experiments on an NVIDIA V100 GPU. We evaluate our method on three benchmark EEG datasets: (i) intracranial recordings from the University of Pennsylvania and the Mayo Clinic, (ii) scalp recordings from the Temple University Hospital of Philadelphia, and (iii) scalp recordings from the Massachusetts Institute of Technology and the Boston Children's Hospital. To assess performance, we report accuracy, precision, recall, Area under the Receiver Operating Characteristics Curve (AUC), and p-value under the Welch t-test for distinguishing seizure vs. non-seizure EEG windows. Our approach can successfully distinguish seizures from non-seizure activity, with up to 0.83 AUC on intracranial recordings. Moreover, our algorithm has the potential for real-time inference, by processing at least 10 s of EEG in a second.

Conclusion: We take the first successful steps in deep learning-based unsupervised seizure identification on raw EEG. Our approach has the potential of alleviating the burden on clinical experts regarding laborsome EEG inspections for seizures. Furthermore, aiding the identification of early seizures via our method could facilitate successful detection of epilepsy development and initiate antiepileptogenic therapies.

Keywords: EEG; Epilepsy; Seizure; Sparsity; Unsupervised learning; Variational autoencoder.

Fig. 1.

Our Variational Autoencoder architecture. The encoder contains convolutional (conv.), batch-normalization (batch norm.), and fully-connected (FC) layers for latent feature extraction, while the decoder contains convolutional transpose (deconv.) and FC layers for upsampling and reconstruction [50]. Conv. and deconv. layers apply 4 × 4 convolutional filters, with the number of filter channels written next to the filter size. FC layers are described via their output dimension. For each layer, the activation function is written in the end of the corresponding description.

Fig. 2.

Distributions of the seizure identification performance metrics of our VAE-based unsupervised method. For each metric, the line inside each box indicates the median, upper and lower limits of each box indicate the upper and lower quartiles, and upper and lower limits of each vertical line indicate the maximum and minimums attained over the 5 test folds.

Fig. 3.

Example EEG windows and corresponding seizure identifications on UPenn.

Fig. 4.

Latent means predicted from seizure (red) vs. non-seizure (blue) windows on UPenn w.r.t. each pair of 3 dimensions. Dimension is reduced from D = 64 to 3 using the t-SNE algorithm.