An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
A Shoeibi, P Moridian, M Khodatars… - Computers in biology …, 2022 - Elsevier
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
An overview of machine learning methods in enabling IoMT-based epileptic seizure detection
ALN Al-Hajjar, AKM Al-Qurabat - The Journal of Supercomputing, 2023 - Springer
The healthcare industry is rapidly automating, in large part because of the Internet of Things
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
[HTML][HTML] Soft labelling based on triangular distributions for ordinal classification
Recently, solving ordinal classification problems using machine learning and deep learning
techniques has acquired important attention. There are many real-world problems in …
techniques has acquired important attention. There are many real-world problems in …
Seizure types classification by generating input images with in-depth features from decomposed EEG signals for deep learning pipeline
Electroencephalogram (EEG) based seizure types classification has not been addressed
well, compared to seizure detection, which is very important for the diagnosis and prognosis …
well, compared to seizure detection, which is very important for the diagnosis and prognosis …
A novel approach to automatic seizure detection using computer vision and independent component analysis
Objective Epilepsy is a neurological disease that affects~ 50 million people worldwide, 30%
of which have refractory epilepsy and recurring seizures, which may contribute to higher …
of which have refractory epilepsy and recurring seizures, which may contribute to higher …
Deep learning approaches for seizure video analysis: A review
Seizure events can manifest as transient disruptions in the control of movements which may
be organized in distinct behavioral sequences, accompanied or not by other observable …
be organized in distinct behavioral sequences, accompanied or not by other observable …
Tri-SeizureDualNet: A novel multimodal brain seizure detection using triple stream skipped feature extraction module entrenched dual parallel attention transformer
M Sunkara, SR Reeja - Biomedical Signal Processing and Control, 2024 - Elsevier
The timely and accurate detection of epileptic seizures is highly needed to enhance the
quality of a patient's life. The state-of-the-art works to design and utilize many deep learning …
quality of a patient's life. The state-of-the-art works to design and utilize many deep learning …
VSViG: Real-Time Video-Based Seizure Detection via Skeleton-Based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), often …
Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), often …
Privacy-preserving early detection of epileptic seizures in videos
In this work, we contribute towards the development of video-based epileptic seizure
classification by introducing a novel framework (SETR-PKD), which could achieve privacy …
classification by introducing a novel framework (SETR-PKD), which could achieve privacy …
Residual and bidirectional LSTM for epileptic seizure detection
W Zhao, WF Wang, LM Patnaik, BC Zhang… - Frontiers in …, 2024 - frontiersin.org
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic
seizures, which affects over 70 million people in the world. Nonetheless, the visual …
seizures, which affects over 70 million people in the world. Nonetheless, the visual …