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 …
A review of automated sleep stage scoring based on physiological signals for the new millennia
O Faust, H Razaghi, R Barika, EJ Ciaccio… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective Sleep is an important part of our life. That importance is
highlighted by the multitude of health problems which result from sleep disorders. Detecting …
highlighted by the multitude of health problems which result from sleep disorders. Detecting …
An attention-based deep learning approach for sleep stage classification with single-channel EEG
Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …
quality. In this paper, we propose a novel attention-based deep learning architecture called …
A new machine learning technique for an accurate diagnosis of coronary artery disease
Background and objective Coronary artery disease (CAD) is one of the commonest diseases
around the world. An early and accurate diagnosis of CAD allows a timely administration of …
around the world. An early and accurate diagnosis of CAD allows a timely administration of …
[HTML][HTML] SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
Artificial intelligence and internet of things enabled disease diagnosis model for smart healthcare systems
The recent advancements in Internet of Things (IoT), cloud computing, and Artificial
Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By …
Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By …
A deep learning model for automated sleep stages classification using PSG signals
Sleep disorder is a symptom of many neurological diseases that may significantly affect the
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …
quality of daily life. Traditional methods are time-consuming and involve the manual scoring …
Classification of heart sound signals using a novel deep WaveNet model
Background and objectives The high mortality rate and increasing prevalence of heart valve
diseases globally warrant the need for rapid and accurate diagnosis of such diseases …
diseases globally warrant the need for rapid and accurate diagnosis of such diseases …
EEG signal classification using LSTM and improved neural network algorithms
Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …
extraction and classification availability in deep learning algorithms. In this paper, we have …
Scalp EEG classification using deep Bi-LSTM network for seizure detection
X Hu, S Yuan, F Xu, Y Leng, K Yuan, Q Yuan - Computers in Biology and …, 2020 - Elsevier
Automatic seizure detection technology not only reduces workloads of neurologists for
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …