A novel end-to-end network based on a bidirectional GRU and a self-attention mechanism for denoising of electroencephalography signals
W Wang, B Li, H Wang - Neuroscience, 2022 - Elsevier
Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that
carry much information. However, physiological signals from other body regions may readily …
carry much information. However, physiological signals from other body regions may readily …
[PDF][PDF] Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds.
S Balasubramanian, P Rajadurai - International Journal of …, 2024 - academia.edu
The study presents a computer-based automated system that employs machine learning to
classify pulmonary diseases using lung sound data collected from hospitals. Denoising …
classify pulmonary diseases using lung sound data collected from hospitals. Denoising …
Electroencephalogram Signal Classification and Artifact Removal with Deep Networks and Adaptive Thresholding
M Mathe, P Mididoddi, K Battula Tirumala - Journal of Shanghai Jiaotong …, 2023 - Springer
Physiological signals such as electroencephalogram (EEG) signals are often corrupted by
artifacts during the acquisition and processing. Some of these artifacts may deteriorate the …
artifacts during the acquisition and processing. Some of these artifacts may deteriorate the …
Design Consideration And Feature Extraction For Unsupervised Ensemble Based EEG Artifacts Eradication And Classification
VS Chouhan, NK Saluja - Educational Administration: Theory and Practice, 2024 - kuey.net
There is several motion artefacts that might appear in electroencephalography (EEG) data
during recording. There were many ensemble based multimode decomposition methods …
during recording. There were many ensemble based multimode decomposition methods …