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Seizures

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A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significa...

Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient inter...

[Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon ...

Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If success...

Seizure Type Classification Using EEG Based on Gramian Angular Field Transformation and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while most of the works are surrounded by seizure detection only. However, in recent times, few works have be...

Classification of Epileptic Seizure From EEG Signal Based on Hilbert Vibration Decomposition and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from decomposed mono-components of electroencephalogram (EEG) signals. For the decomposition of EEG, Hil...

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the pe...

Towards Deeper Neural Networks for Neonatal Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work proposes a deep neural network architecture which is capable of extracting information from long segment...

A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because...

Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae...