Deep learning for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
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 …
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of
epilepsy. The EEG signal contains information about the electrical activity of the brain …
epilepsy. The EEG signal contains information about the electrical activity of the brain …
Deep learning for electromyographic hand gesture signal classification using transfer learning
In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …
their unparalleled ability to automatically learn discriminant features from large amounts of …
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
Electroencephalographic (EEG) recordings generate an electrical map of the human brain
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
A comparative analysis of signal processing and classification methods for different applications based on EEG signals
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …
currents that are generated due to the synchronized activity by a group of specialized …
Systematic review on resting‐state EEG for Alzheimer's disease diagnosis and progression assessment
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of
the more than 46 million dementia cases estimated worldwide. Although there is no cure for …
the more than 46 million dementia cases estimated worldwide. Although there is no cure for …
Detection of epileptic seizure using pretrained deep convolutional neural network and transfer learning
Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the
attending physician. However, the human factor may cause erroneous diagnosis in the …
attending physician. However, the human factor may cause erroneous diagnosis in the …
A new neural dynamic classification algorithm
The keys for the development of an effective classification algorithm are: 1) discovering
feature spaces with large margins between clusters and close proximity of the classmates …
feature spaces with large margins between clusters and close proximity of the classmates …
A multi-view deep learning framework for EEG seizure detection
The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …