Machine learning and wearable devices of the future
Abstract Machine learning (ML) is increasingly recognized as a useful tool in healthcare
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
applications, including epilepsy. One of the most important applications of ML in epilepsy is …
[HTML][HTML] Machine learning for detection of interictal epileptiform discharges
C da Silva Lourenço, MC Tjepkema-Cloostermans… - Clinical …, 2021 - Elsevier
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of
epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased …
epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased …
Automated interpretation of clinical electroencephalograms using artificial intelligence
Importance Electroencephalograms (EEGs) are a fundamental evaluation in neurology but
require special expertise unavailable in many regions of the world. Artificial intelligence (AI) …
require special expertise unavailable in many regions of the world. Artificial intelligence (AI) …
Resting-state oscillations reveal disturbed excitation–inhibition ratio in Alzheimer's disease patients
An early disruption of neuronal excitation–inhibition (E–I) balance in preclinical animal
models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure …
models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure …
Biot: Biosignal transformer for cross-data learning in the wild
Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard
Objective To validate an artificial intelligence-based computer algorithm for detection of
epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy …
epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy …
Focal sleep spindle deficits reveal focal thalamocortical dysfunction and predict cognitive deficits in sleep activated developmental epilepsy
Childhood epilepsy with centrotemporal spikes (CECTS) is the most common focal epilepsy
syndrome, yet the cause of this disease remains unknown. Now recognized as a mild …
syndrome, yet the cause of this disease remains unknown. Now recognized as a mild …
Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography
É Lemoine, D Toffa, G Pelletier-Mc Duff, AQ Xu… - Scientific Reports, 2023 - nature.com
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy.
Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure …
Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure …
Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks
Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges
(IEDs) as distinctive biomarkers of epilepsy has various limitations, including time …
(IEDs) as distinctive biomarkers of epilepsy has various limitations, including time …
A Flexible Bidirectional Interface with Integrated Multimodal Sensing and Haptic Feedback for Closed‐Loop Human–Machine Interaction
Human–machine interaction (HMI) establishes an interconnected bridge between humans
and robots and plays a significant role in industry and medical fields. However, the …
and robots and plays a significant role in industry and medical fields. However, the …