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Seizure Detection is a binary supervised classification problem with the aim of classifying between seizure and non-seizure states of a patient.

Source: ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

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Datasets

Greatest papers with code

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

18 Dec 2016pulp-platform/pulp

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.

EEG FACE DETECTION SEIZURE DETECTION

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

16 May 2018dan-zam/cdg

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.

SEIZURE DETECTION

Learning Robust Features using Deep Learning for Automatic Seizure Detection

31 Jul 2016Sharad24/Epileptic-Seizure-Detection

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.

EEG SEIZURE DETECTION

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

8 Mar 2019IBM/seizure-type-classification-tuh

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.

EEG KNOWLEDGE DISTILLATION SEIZURE DETECTION

Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark

4 Feb 2019IBM/seizure-type-classification-tuh

On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.

EEG SEIZURE DETECTION

Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

22 Jul 2019dapascual/GAN_epilepsy

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.

EEG SEIZURE DETECTION

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

18 Sep 2019xiangzhang1015/adversarial_seizure_detection

Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.

EEG FEATURE ENGINEERING REPRESENTATION LEARNING SEIZURE DETECTION