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Seizures

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Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

International journal of neural systems
ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately p...

Applications for Deep Learning in Epilepsy Genetic Research.

International journal of molecular sciences
Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy....

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery.

Memristive Neural Networks for Predicting Seizure Activity.

Sovremennye tekhnologii v meditsine
UNLABELLED: is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artific...

Deep learning in neuroimaging of epilepsy.

Clinical neurology and neurosurgery
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional m...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Nature communications
Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for ...

Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

Epilepsia
OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natura...

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such d...

From basic sciences and engineering to epileptology: A translational approach.

Epilepsia
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (IC...

Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of ...