AIMC Topic: Epilepsy

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Epileptic Seizures Prediction Using Machine Learning Methods.

Computational and mathematical methods in medicine
Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning technique...

Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study.

Journal of medical engineering & technology
In this work, we have used a time-frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared to other proposed methods because of its algorithmic elegance and accuracy. A wavelet is a mathematic...

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

EBioMedicine
BACKGROUND: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an...

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Computers in biology and medicine
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epi...

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

IEEE transactions on bio-medical engineering
OBJECTIVE: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the class...

Phenomenological network models: Lessons for epilepsy surgery.

Epilepsia
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational m...

Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy.

Journal of neuroscience methods
BACKGROUND: Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively...

Evolving Network Model That Almost Regenerates Epileptic Data.

Neural computation
In many realistic networks, the edges representing the interactions between nodes are time varying. Evidence is growing that the complex network that models the dynamics of the human brain has time-varying interconnections, that is, the network is ev...