Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images
Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first
identified in the city of Wuhan, China somewhere in the early December 2019. It affects the …
identified in the city of Wuhan, China somewhere in the early December 2019. It affects the …
Neurodegenerative disease detection and severity prediction using deep learning approaches
Neurodegenerative diseases (NDDs) such as amyotrophic lateral sclerosis (ALS),
Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves …
Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves …
Novel CEFNet framework for lung disease detection and infection region identification
Lung disease encompasses various disorders that result in impaired lung function and
breathing difficulties. We propose a two-stage pipeline for automated lung disease detection …
breathing difficulties. We propose a two-stage pipeline for automated lung disease detection …
Recognition of lower limb movements using empirical mode decomposition and k-nearest neighbor entropy estimator with surface electromyogram signals
Lower limb movement recognition is critical to the daily care of the elderly, the weak, and the
disabled. Surface electromyogram (sEMG) signals reflect the intention of human movements …
disabled. Surface electromyogram (sEMG) signals reflect the intention of human movements …
A novel time-frequency model, analysis and parameter estimation approach: Towards multiple close and crossed chirp modes
Y Wang, W Yang, D Li, JQ Zhang - Signal Processing, 2022 - Elsevier
In this paper, a novel model for online time-frequency representation and analysis with
multiple close and crossed chirp modes is proposed. It is shown that, when a signal is …
multiple close and crossed chirp modes is proposed. It is shown that, when a signal is …
Direct signal separation via extraction of local frequencies with adaptive time-varying parameters
Real-world phenomena that can be formulated as signals are often affected by a number of
factors and appear as multi-component modes. To understand and process such …
factors and appear as multi-component modes. To understand and process such …
Accurate AM-FM signal demodulation and separation using nonparametric regularization method
X Hu, S Peng, B Guo, P Xu - Signal Processing, 2021 - Elsevier
In this paper we propose a novel adaptive nonparametric regularization (NPR) method for
solving optimization problems with linear constraint. The regular parameter in NPR …
solving optimization problems with linear constraint. The regular parameter in NPR …
Glaucoma detection using SS-QB-VMD-based fine sub-band images from fundus images
Worldwide, glaucoma is a type of eye disease, which causes loss of vision by damaging
optic nerves within the eye. Available glaucoma detection techniques are less accurate. This …
optic nerves within the eye. Available glaucoma detection techniques are less accurate. This …
AFM signal model for dysarthric speech classification using speech biomarkers
SM Shabber, EP Sumesh - Frontiers in Human Neuroscience, 2024 - frontiersin.org
Neurological disorders include various conditions affecting the brain, spinal cord, and
nervous system which results in reduced performance in different organs and muscles …
nervous system which results in reduced performance in different organs and muscles …