Epilepsy detection from EEG using complex network techniques: A review
Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-
third of epileptic patients experience seizures attack even with medicated treatment. The …
third of epileptic patients experience seizures attack even with medicated treatment. The …
[HTML][HTML] Epileptic seizure detection and experimental treatment: a review
One-fourths of the patients have medication-resistant seizures and require seizure detection
and treatment continuously to cope with sudden seizures. Seizures can be detected by …
and treatment continuously to cope with sudden seizures. Seizures can be detected by …
1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
Structural damage detection has been an interdisciplinary area of interest for various
engineering fields. While the available damage detection methods have been in the process …
engineering fields. While the available damage detection methods have been in the process …
A decision support system for automated identification of sleep stages from single-channel EEG signals
A decision support system for automated detection of sleep stages can alleviate the burden
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms
PA Karthick, DM Ghosh, S Ramakrishnan - Computer methods and …, 2018 - Elsevier
Background and objective Surface electromyography (sEMG) based muscle fatigue
research is widely preferred in sports science and occupational/rehabilitation studies due to …
research is widely preferred in sports science and occupational/rehabilitation studies due to …
Deep convolutional neural network for classification of sleep stages from single-channel EEG signals
Using a smart method for automatic diagnosis in medical applications, such as sleep stage
classification is considered as one of the important challenges of the last few years which …
classification is considered as one of the important challenges of the last few years which …
Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach
Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task
due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected …
due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected …
[HTML][HTML] EEG-based emotion recognition using quadratic time-frequency distribution
Accurate recognition and understating of human emotions is an essential skill that can
improve the collaboration between humans and machines. In this vein …
improve the collaboration between humans and machines. In this vein …
Adversarial representation learning for robust patient-independent epileptic seizure detection
Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous
seizures, which affects about one percent of the worlds population. Most of the current …
seizures, which affects about one percent of the worlds population. Most of the current …
Convolutional neural network based emotion classification using electrodermal activity signals and time-frequency features
N Ganapathy, YR Veeranki, R Swaminathan - Expert Systems with …, 2020 - Elsevier
In this work, an attempt has been made to classify emotional states using Electrodermal
Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA …
Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA …