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Seizures

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Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

Scientific reports
Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring met...

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.

Computational and mathematical methods in medicine
Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consum...

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

Computational and mathematical methods in medicine
The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and he...

Automatic Seizure Detection using Fully Convolutional Nested LSTM.

International journal of neural systems
The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are base...

Molecular and DNA Artificial Neural Networks via Fractional Coding.

IEEE transactions on biomedical circuits and systems
This article considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using f...

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the dat...

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we pro...

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects.

Sensors (Basel, Switzerland)
The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool ava...

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous seizures, which affects about one percent of the worlds population. Most of the current seizure detection approaches strongly rely on patient history records a...

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Neural networks : the official journal of the International Neural Network Society
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been ma...