[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 developing novel and efficient …
disrupts the lifestyle of affected individuals. Toward developing novel and efficient …
A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
[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 …
Wavelet transform application for/in non-stationary time-series analysis: A review
Non-stationary time series (TS) analysis has gained an explosive interest over the recent
decades in different applied sciences. In fact, several decomposition methods were …
decades in different applied sciences. In fact, several decomposition methods were …
A survey on wearable sensor modality centred human activity recognition in health care
Increased life expectancy coupled with declining birth rates is leading to an aging
population structure. Aging-caused changes, such as physical or cognitive decline, could …
population structure. Aging-caused changes, such as physical or cognitive decline, could …
A review on machine learning for EEG signal processing in bioengineering
MP Hosseini, A Hosseini, K Ahi - IEEE reviews in biomedical …, 2020 - ieeexplore.ieee.org
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
P Boonyakitanont, A Lek-Uthai, K Chomtho… - … Signal Processing and …, 2020 - Elsevier
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 …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
Short term electricity load forecasting using a hybrid model
J Zhang, YM Wei, D Li, Z Tan, J Zhou - Energy, 2018 - Elsevier
Short term electricity load forecasting is one of the most important issue for all market
participants. Short term electricity load is affected by natural and social factors, which makes …
participants. Short term electricity load is affected by natural and social factors, which makes …
A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting
Renewable energy has made a significant contribution to global power generation.
Therefore, accurate mid-to-long term renewable energy generation forecasting is becoming …
Therefore, accurate mid-to-long term renewable energy generation forecasting is becoming …