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 …
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 …
Fog computing in healthcare–a review and discussion
Fog computing is an architectural style in which network components between devices and
the cloud execute application-specific logic. We present the first review on fog computing …
the cloud execute application-specific logic. We present the first review on fog computing …
Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals
Objective Automatic detection of epileptic seizures based on deep learning methods
received much attention last year. However, the potential of deep neural networks in seizure …
received much attention last year. However, the potential of deep neural networks in seizure …
Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security
The industrial control systems are facing an increasing number of sophisticated cyber
attacks that can have very dangerous consequences on humans and their environments. In …
attacks that can have very dangerous consequences on humans and their environments. In …
Optimized deep learning for EEG big data and seizure prediction BCI via internet of things
MP Hosseini, D Pompili, K Elisevich… - … Transactions on Big …, 2017 - ieeexplore.ieee.org
A brain-computer interface (BCI) for seizure prediction provides a means of controlling
epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected …
epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected …
Epileptic seizure detection: A deep learning approach
Epilepsy is the second most common brain disorder after migraine. Automatic detection of
epileptic seizures can considerably improve the patients' quality of life. Current …
epileptic seizures can considerably improve the patients' quality of life. Current …
Cloud-based deep learning of big EEG data for epileptic seizure prediction
MP Hosseini, H Soltanian-Zadeh… - 2016 IEEE global …, 2016 - ieeexplore.ieee.org
Developing a Brain-Computer Interface (BCI) for seizure prediction can help epileptic
patients have a better quality of life. However, there are many difficulties and challenges in …
patients have a better quality of life. However, there are many difficulties and challenges in …
Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing
Background and objective Multimodal data analysis and large-scale computational
capability is entering medicine in an accelerative fashion and has begun to influence …
capability is entering medicine in an accelerative fashion and has begun to influence …
Non-intrusive energy disaggregation using non-negative matrix factorization with sum-to-k constraint
A Rahimpour, H Qi, D Fugate… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting
device-level energy consumption information by monitoring the aggregated signal at one …
device-level energy consumption information by monitoring the aggregated signal at one …