Blood pressure detection using CNN-LSTM model
Blood pressure (BP) is a key indication that needs to be checked on a regular basis. For
maintaining a healthy life normal blood pressure is essential, and a continuous change can …
maintaining a healthy life normal blood pressure is essential, and a continuous change can …
A convolutional neural network approach for diabetic retinopathy classification
Diabetic Retinopathy (DR) is a kind of problem which affects diabetic patients, particularly
those at their age of working, and can result in vision impairment and possibly irreversible …
those at their age of working, and can result in vision impairment and possibly irreversible …
Feature engineering of EEG applied to mental disorders: a systematic mapping study
Around a third of the total population of Europe suffers from mental disorders. The use of
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …
Liver disease prediction using machine learning classification techniques
Machine Learning is a process which is used to discover patterns in huge data/large data
set to enable decision, thereby allowing machines to go through a learning process (ie …
set to enable decision, thereby allowing machines to go through a learning process (ie …
Automated Seizure Detection using Theta Band
The EEG signal is made up of numerous frequency bands that characterize human
behaviours like emotion, attentiveness, and sleep status, among others. In order to detect …
behaviours like emotion, attentiveness, and sleep status, among others. In order to detect …
A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
diagnosis. Scholars have suggested automated techniques to detect the ailment due to the …
A 2D CNN-LSTM hybrid algorithm using time series segments of EEG data for motor imagery classification
J Wang, S Cheng, J Tian, Y Gao - Biomedical Signal Processing and …, 2023 - Elsevier
Motor imagery-based brain–computer interaction (MI-BCI) converts human neural activity
into computational information, often used as commands, by recognizing …
into computational information, often used as commands, by recognizing …
Hypertension classification using machine learning part II
High blood pressure (BP) or hypertension is a dangerous and deadly condition which can
lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying …
lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying …
CNN based framework for detection of epileptic seizures
Epilepsy is a common neurological disease that uses electroencephalogram (EEG) data for
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports …
Deep DR: detection of diabetic retinopathy using a convolutional neural network
N Nasir, P Oswald, O Alshaltone… - 2022 Advances in …, 2022 - ieeexplore.ieee.org
Diabetic retinopathy (DR) is a consequence of diabetes that affects the back of the eye due
to excessive blood sugar levels. If left misdiagnosed and untreated, it might result in …
to excessive blood sugar levels. If left misdiagnosed and untreated, it might result in …