EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
We introduced a multilayer perceptron neural network (MLPNN) based classification model
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
IoT based smart monitoring of patients' with acute heart failure
The prediction of heart failure survivors is a challenging task and helps medical
professionals to make the right decisions about patients. Expertise and experience of …
professionals to make the right decisions about patients. Expertise and experience of …
Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application
As artificial intelligence in medical imaging is used to diagnose many diseases, it can also
be employed to diagnose whether a person has periodontal bone loss or not. Accurate and …
be employed to diagnose whether a person has periodontal bone loss or not. Accurate and …
EEG-based neonatal sleep-wake classification using multilayer perceptron neural network
Objective: Classification of sleep-wake states using multichannel electroencephalography
(EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) …
(EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) …
EEG signal classification for epilepsy diagnosis via optimum path forest–A systematic assessment
Epilepsy refers to a set of chronic neurological syndromes characterized by transient and
unexpected electrical disturbances of the brain. The detailed analysis of the …
unexpected electrical disturbances of the brain. The detailed analysis of the …
Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform
X Zhang, X Yan - Accident Analysis & Prevention, 2023 - Elsevier
Unsignalized intersection collision has been one of the most dangerous accidents in the
world. How to identify road hazards and predict the potential intersection collision ahead are …
world. How to identify road hazards and predict the potential intersection collision ahead are …
Block term decomposition for modelling epileptic seizures
Recordings of neural activity, such as EEG, are an inherent mixture of different ongoing
brain processes as well as artefacts and are typically characterised by low signal-to-noise …
brain processes as well as artefacts and are typically characterised by low signal-to-noise …
[HTML][HTML] A review of developments of EEG-based automatic medical support systems for epilepsy diagnosis and seizure detection
Y Song - Journal of Biomedical Science and Engineering, 2011 - scirp.org
Epilepsy is one of the most common neurological disorders-approximately one in every 100
people worldwide are suffering from it. The electroencephalogram (EEG) is the most …
people worldwide are suffering from it. The electroencephalogram (EEG) is the most …
Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques
The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions,
and precautionary measures related to the disease on social media platforms. Despite the …
and precautionary measures related to the disease on social media platforms. Despite the …
Predicting complete ground reaction forces and moments during gait with insole plantar pressure information using a wavelet neural network
In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments
(GRMs) that occur during human gait are measured using a force plate, which are expensive …
(GRMs) that occur during human gait are measured using a force plate, which are expensive …