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Seizures

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Probabilistic prediction of Epileptic Seizures using SVM.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this paper, an algorithm based on the linear Support Vector Machine (SVM) tool was proposed to classify intracranial electroencephalography (iEEG) signals as ictal or interictal to perform human seizure prediction, efficiently. Various univariate ...

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an important research direction. A novel automatic epilepsy detection method based on multi-weight transition network was proposed in this paper. The epilept...

A convolutional neural network based framework for classification of seizure types.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to clas...

Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal itself and of the generated abnormalities. In this paper, we investigate several deep network archit...

A Multi-View Deep Learning Framework for EEG Seizure Detection.

IEEE journal of biomedical and health informatics
The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual in...

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

Neurosurgery
BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but i...

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional recep...

Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a sh...

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

IEEE transactions on biomedical circuits and systems
Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (...

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
 During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce...