AIMC Topic: Signal Processing, Computer-Assisted

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An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Medical & biological engineering & computing Nov 29, 2024
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and sel...
Algorithms Humans Electroencephalography Machine Learning Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination
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A Novel Real-time Phase Prediction Network in EEG Rhythm.

Neuroscience bulletin Nov 29, 2024
Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-station...
Electroencephalography Signal Processing, Computer-Assisted Brain Waves Brain Adult Male Neural Networks, Computer Machine Learning Humans
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Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.

Medical engineering & physics Nov 29, 2024
Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data are susceptible to various types of noise during acquisition. To tackle the problem, Subspace Search ...
Arrhythmias, Cardiac Time Factors Humans Fourier Analysis Machine Learning Electrocardiography Signal Processing, Computer-Assisted
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Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?

Computer methods and programs in biomedicine Nov 28, 2024
BACKGROUND: Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to...
Algorithms Humans Male Atrial Fibrillation Photoplethysmography Neural Networks, Computer Female Middle Aged Aged Signal Processing, Computer-Assisted Machine Learning Smartphone
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MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland) Nov 28, 2024
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...
Brain-Computer Interfaces Imagination Electroencephalography Humans Algorithms Signal Processing, Computer-Assisted Neural Networks, Computer Brain Attention Lower Extremity
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Boosting Cardiac Color Doppler Frame Rates With Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 27, 2024
Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ...
Echocardiography, Doppler, Color Image Processing, Computer-Assisted Signal Processing, Computer-Assisted Heart Humans Neural Networks, Computer Algorithms Deep Learning
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Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 27, 2024
Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across h...
Humans Algorithms Signal Processing, Computer-Assisted Heart Feasibility Studies Echocardiography Deep Learning Image Processing, Computer-Assisted
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Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.

Journal of neuroscience methods Nov 23, 2024
Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this pape...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Brain Electroencephalography Humans Neural Networks, Computer Algorithms
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Classifying vocal responses of broilers to environmental stressors via artificial neural network.

Animal : an international journal of animal bioscience Nov 22, 2024
Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transforme...
Vocalization, Animal Signal Processing, Computer-Assisted Stress, Physiological Neural Networks, Computer Animals Animal Husbandry Chickens
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Learning a Hand Model From Dynamic Movements Using High-Density EMG and Convolutional Neural Networks.

IEEE transactions on bio-medical engineering Nov 21, 2024
OBJECTIVE: Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. This work presents a deep learning method that can decode the electrophysiological activity of the forearm muscles into the movements of the human han...
Humans Adult Biomechanical Phenomena Male Neural Networks, Computer Female Young Adult Electromyography Movement Deep Learning Convolutional Neural Networks Muscle, Skeletal Signal Processing, Computer-Assisted Hand
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