AIMC Topic: Signal Processing, Computer-Assisted

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Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU.

PloS one Jun 24, 2025
Shortwave communication plays a vital role in disaster relief and remote communications due to its long-range capabilities and resilience to interference. However, challenges such as multipath propagation, frequency-selective fading, and low signal-t...
Deep Learning Humans Signal Processing, Computer-Assisted Algorithms Signal-To-Noise Ratio
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Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.

PloS one Jun 17, 2025
Arrhythmia is a prevalent cardiac disorder that can lead to severe complications such as stroke and cardiac arrest. While deep learning has advanced automated ECG analysis, challenges remain in accurately classifying arrhythmias due to signal variabi...
Convolutional Neural Networks Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Algorithms Databases, Factual Deep Learning Humans Neural Networks, Computer
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Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.

PloS one Jun 13, 2025
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This high...
Electroencephalography Signal Processing, Computer-Assisted Wavelet Analysis Polysomnography Female Humans Sleep Deprivation Adult Neural Networks, Computer Male Algorithms
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Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.

Physiological measurement Jun 10, 2025
To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures.iEEG data were collected from si...
Signal Processing, Computer-Assisted Automation Electrocorticography Animals Electroencephalography Seizures Machine Learning Primates
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PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.

Neural networks : the official journal of the International Neural Network Society May 19, 2025
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive ...
Electroencephalography Signal Processing, Computer-Assisted Electromyography Humans Neural Networks, Computer Eye-Tracking Technology
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Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Sensors (Basel, Switzerland) May 18, 2025
Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to promin...
Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Machine Learning Algorithms Brain Humans Neural Networks, Computer
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Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.

Nature cardiovascular research May 16, 2025
Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncoverin...
Machine Learning Phenotype Female Aged Predictive Value of Tests Humans Adult Signal Processing, Computer-Assisted Male Heart Conduction System Middle Aged Heart Rate Magnetic Resonance Imaging Electrocardiography Sex Factors Action Potentials Age Factors Myocardial Ischemia Obesity
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Continuous Joint Kinematics Prediction Using GAT-LSTM Framework Based on Muscle Synergy and Sparse sEMG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society May 8, 2025
sEMG signals hold significant potential for motion prediction, with promising applications in areas such as rehabilitation, sports training, and human-computer interaction. However, achieving robust prediction accuracy remains a critical challenge, a...
Movement Young Adult Electromyography Algorithms Electrodes Male Adult Humans Muscle, Skeletal Joints Female Biomechanical Phenomena Signal Processing, Computer-Assisted Reproducibility of Results Neural Networks, Computer
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Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Scientific reports May 7, 2025
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-la...
Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted Epilepsy Seizures
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CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection.

IEEE journal of biomedical and health informatics May 6, 2025
Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significant...
Signal Processing, Computer-Assisted Magnetoencephalography Brain Deep Learning Epilepsy Humans Algorithms
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