Seizure Detection is a binary supervised classification problem with the aim of classifying between seizure and non-seizure states of a patient.
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The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline.
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.
On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.
In this work, we generate synthetic seizure-like brain electrical activities, i. e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data.
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.