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 …
AI-based epileptic seizure detection and prediction in internet of healthcare things: a systematic review
Epilepsy is a neurological condition affecting around 50 million individuals worldwide,
reported by the World Health Organization. This is identified as a hypersensitive disease by …
reported by the World Health Organization. This is identified as a hypersensitive disease by …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
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 …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …
Accuracy enhancement of epileptic seizure detection: a deep learning approach with hardware realization of STFT
Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective
way of predicting such seizure and it is used widely in recent days for classifying and …
way of predicting such seizure and it is used widely in recent days for classifying and …
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 …
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 …