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[HTML][HTML] Artificial intelligence in healthcare: review and prediction case studies
Artificial intelligence (AI) has been develo** rapidly in recent years in terms of software
algorithms, hardware implementation, and applications in a vast number of areas. In this …
algorithms, hardware implementation, and applications in a vast number of areas. In this …
[Retracted] EEG‐Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
I Ahmad, X Wang, M Zhu, C Wang, Y Pi… - Computational …, 2022 - Wiley Online Library
Epileptic seizure is one of the most chronic neurological diseases that instantaneously
disrupts the lifestyle of affected individuals. Toward develo** novel and efficient …
disrupts the lifestyle of affected individuals. Toward develo** novel and efficient …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
Efficient epileptic seizure prediction based on deep learning
H Daoud, MA Bayoumi - IEEE transactions on biomedical …, 2019 - ieeexplore.ieee.org
Epilepsy is one of the world's most common neurological diseases. Early prediction of the
incoming seizures has a great influence on epileptic patients' life. In this paper, a novel …
incoming seizures has a great influence on epileptic patients' life. In this paper, a novel …
Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram
Seizure prediction has attracted growing attention as one of the most challenging predictive
data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic …
data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic …
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
Since the manual detection of electrographic seizures in continuous electroencephalogram
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal
X Qiu, F Yan, H Liu - Biomedical Signal Processing and Control, 2023 - Elsevier
Epileptic seizures can affect the patient's physical function and cause irreversible damage to
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism
Epilepsy is considered as a serious brain disorder in which patients frequently experience
seizures. The seizures are defined as the unexpected electrical changes in brain neural …
seizures. The seizures are defined as the unexpected electrical changes in brain neural …
EEG datasets for seizure detection and prediction—A review
Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop
seizure detection and prediction algorithms using machine learning (ML) techniques with …
seizure detection and prediction algorithms using machine learning (ML) techniques with …
Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features
Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …