In a paper published on the preprint server Arxiv.org this week, IBM researchers describe SeizureNet, a machine learning framework that learns the features of seizures to classify various types. They say that it achieves state-of-the-art classification accuracy on a popular data set, and that it helps to improve the classification accuracy of smaller networks for applications with low memory and faster inference. If the claims stand up to academic scrutiny, the framework could, for instance, help the over 3.4 million people with epilepsy better understand the factors that trigger their seizures. The World Health Organization estimates that up to 70% of people living with epilepsy could live seizure-free if properly diagnosed and treated. SeizureNet is a machine learning framework consisting of individual classifiers (specifically convolutional neural networks) that learn the features of electroencephalograms (EEGs) -- i.e., tests that evaluate the electrical activity in the brain -- to predict seizure types.

Automatic epileptic seizure analysis is important because the differentiation of neural patterns among different patients can be used to classify people with specific types of epilepsy. This could enable more efficient management of the disease. Automatic seizure type classification using clinical electroencephalograms (EEGs) is challenging due to factors such as low signal to noise ratios, signal artefacts, high variance in the seizure semiology among individual epileptic patients, and limited clinical data constraints. To overcome these challenges, in this paper, we present a deep learning based framework which uses a Convolutional Neural Network (CNN) with dense connections and learns highly robust features at different spatial and temporal resolutions of the EEG data spectrum for accurate cross-patient seizure type classification. We evaluate our framework for seizure type classification and seizure detection on the recently released TUH EEG Seizure Corpus, where our framework achieves overall weighted f 1 scores of up to 0.90 and 0.88, thereby setting new benchmarks on the dataset.

Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation. In this paper we present a convolutional neural network for the early detection of seizures from intracranial EEG signals, designed specifically for this purpose. In addition, we investigate approximations to comply with hardware limits while preserving accuracy. We compare our approach to three previously proposed convolutional neural networks and a feature-based SVM classifier with respect to detection accuracy, latency and computational needs. Evaluation is based on a comprehensive database with long-term EEG recordings. The proposed method outperforms the other detectors with a median sensitivity of 0.96, false detection rate of 10.1 per hour and median detection delay of 3.7 seconds, while being the only approach suited to be realized on a low power microcontroller due to its parsimonious use of computational and memory resources.