[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 on the role of machine learning in enabling IoT based healthcare applications
HK Bharadwaj, A Agarwal, V Chamola… - IEEE …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare
sector. The branch of IoT dedicated towards medical science is at times termed as …
sector. The branch of IoT dedicated towards medical science is at times termed as …
Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
Automated seizure detection using limited-channel EEG and non-linear dimension reduction
Electroencephalography (EEG) is an essential component in evaluation of epilepsy.
However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither …
However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither …
A non-EEG biosignals dataset for assessment and visualization of neurological status
J Birjandtalab, D Cogan, MB Pouyan… - … Workshop on Signal …, 2016 - ieeexplore.ieee.org
Neurological assessment can be used to monitor a person's neurological status. In this
paper, we report collection and analysis of a multimodal dataset of Non-EEG physiological …
paper, we report collection and analysis of a multimodal dataset of Non-EEG physiological …
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
Background There is growing interest in utilizing artificial intelligence, and particularly deep
learning, for computer vision in histopathology. While accumulating studies highlight expert …
learning, for computer vision in histopathology. While accumulating studies highlight expert …
Frontal EEG asymmetry of emotion for the same auditory stimulus
Emotions play an important role in human interaction and decision-making processes.
Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion …
Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion …
Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG …
A Arora, JJ Lin, A Gasperian, J Maldjian… - Journal of neural …, 2018 - iopscience.iop.org
Objective. We sought to test the performance of three strategies for binary classification
(logistic regression, support vector machines, and deep learning) for the problem of …
(logistic regression, support vector machines, and deep learning) for the problem of …
Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases
Free-text clinical notes in electronic health records are more difficult for data mining while
the structured diagnostic codes can be missing or erroneous. To improve the quality of …
the structured diagnostic codes can be missing or erroneous. To improve the quality of …
Classification of EEG signals for epileptic seizures using feature dimension reduction algorithm based on LPP
Y Liu, B Jiang, J Feng, J Hu, H Zhang - Multimedia Tools and Applications, 2021 - Springer
Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is
a beneficial practice which adopts machine learning to increase the recognition rate and …
a beneficial practice which adopts machine learning to increase the recognition rate and …