Artificial intelligence in epilepsy—applications and pathways to the clinic
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy
have increased exponentially over the past decade. Integration of AI into epilepsy …
have increased exponentially over the past decade. Integration of AI into epilepsy …
DICE-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals
Objective: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
a significant percentage of the elderly. EEG has emerged as a promising tool for the timely …
Systematic reviews of machine learning in healthcare: a literature review
K Kolasa, B Admassu… - Expert Review of …, 2024 - Taylor & Francis
Introduction The increasing availability of data and computing power has made machine
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …
A dataset of scalp EEG recordings of Alzheimer's disease, frontotemporal dementia and healthy subjects from routine EEG
Recently, there has been a growing research interest in utilizing the electroencephalogram
(EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article …
(EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article …
Two-stage approach with combination of outlier detection method and deep learning enhances automatic epileptic seizure detection
Many approaches to automated epileptic seizure detection share a common challenge—the
trade-off between recall and precision. This study aims to develop a novel approach for …
trade-off between recall and precision. This study aims to develop a novel approach for …
Optimization of epilepsy detection method based on dynamic EEG channel screening
Y Song, C Fan, X Mao - Neural Networks, 2024 - Elsevier
To decrease the interference in the process of epileptic feature extraction caused by
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images
Epilepsy is a prevalent neurological disorder with considerable risks, including physical
impairment and irreversible brain damage from seizures. Given these challenges, the …
impairment and irreversible brain damage from seizures. Given these challenges, the …
Enhanced Alzheimer's disease and Frontotemporal Dementia EEG Detection: Combining lightGBM Gradient Boosting with Complexity Features
Alzheimer's disease and Frontotemporal dementia are the two most reported dementia
cases. They both are neurodegenerative disorders without cure while existing treatments …
cases. They both are neurodegenerative disorders without cure while existing treatments …
The use of CNNs in VR/AR/MR/XR: a systematic literature review
This study offers a systematic literature review on the application of Convolutional Neural
Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality …
Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality …
[HTML][HTML] A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that
poses critical challenges in global healthcare due to its increasing prevalence and severity …
poses critical challenges in global healthcare due to its increasing prevalence and severity …