Advances and disturbances in sEMG-based intentions and movements recognition: A review
Surface EMG-based gestures recognition systems are helping the disable to enjoy a better
life. Academic institutes and commercial companies have been developing a lot of sEMG …
life. Academic institutes and commercial companies have been developing a lot of sEMG …
Use of advanced materials and artificial intelligence in electromyography signal detection and interpretation
Electromyography (EMG) is an integral part of many biomedical and healthcare applications.
It has been used as a metric for tracking rehabilitation progress and identifying diseases that …
It has been used as a metric for tracking rehabilitation progress and identifying diseases that …
EMGHandNet: A hybrid CNN and Bi-LSTM architecture for hand activity classification using surface EMG signals
NK Karnam, SR Dubey, AC Turlapaty… - Biocybernetics and …, 2022 - Elsevier
Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …
classification of hand activities from surface Electromyography (sEMG) signals. However …
Self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for classification of brain tumor on MRI …
N Nagarani, R Karthick, MSC Sophia… - … Signal Processing and …, 2024 - Elsevier
This manuscript proposes a self-attention based progressive generative adversarial network
optimized with momentum search optimization algorithm for brain tumor classification on …
optimized with momentum search optimization algorithm for brain tumor classification on …
EEG Signal denoising using hybrid approach of Variational Mode Decomposition and wavelets for depression
Background Artifact contamination reduces the accuracy of various EEG based
neuroengineering applications. With time, biomedical signal denoising has been the utmost …
neuroengineering applications. With time, biomedical signal denoising has been the utmost …
A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals
K Rezaee, S Savarkar, X Yu, J Zhang - Biomedical Signal Processing and …, 2022 - Elsevier
Parkinson's disease (PD) is known as a rampant neurodegenerative disorder, which has
afflicted approximately 10 million people throughout the world. Surface Electromyography …
afflicted approximately 10 million people throughout the world. Surface Electromyography …
Novel multi center and threshold ternary pattern based method for disease detection method using voice
Smart health is one of the most popular and important components of smart cities. It is a
relatively new context-aware healthcare paradigm influenced by several fields of expertise …
relatively new context-aware healthcare paradigm influenced by several fields of expertise …
Human knee abnormality detection from imbalanced sEMG data
The classification of imbalanced datasets, especially in medicine, is a major problem in data
mining. Such a problem is evident in analyzing normal and abnormal subjects about knee …
mining. Such a problem is evident in analyzing normal and abnormal subjects about knee …
Automatic COVID-19 detection using exemplar hybrid deep features with X-ray images
COVID-19 and pneumonia detection using medical images is a topic of immense interest in
medical and healthcare research. Various advanced medical imaging and machine learning …
medical and healthcare research. Various advanced medical imaging and machine learning …
Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods
P Li, Y Hu, ZP Liu - Biomedical Signal Processing and Control, 2021 - Elsevier
Electrocardiogram (ECG) and phonocardiogram (PCG) play important roles in early
prevention and diagnosis of cardiovascular diseases (CVDs). As the development of …
prevention and diagnosis of cardiovascular diseases (CVDs). As the development of …