AIMC Topic: Signal Processing, Computer-Assisted

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Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.

Sensors (Basel, Switzerland) Jun 4, 2024
Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed meth...
Electromyography Pattern Recognition, Automated Gestures Signal Processing, Computer-Assisted Hand Machine Learning Algorithms Deep Learning Humans Neural Networks, Computer
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ECG autoencoder based on low-rank attention.

Scientific reports Jun 4, 2024
The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gai...
Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Neural Networks, Computer Machine Learning Algorithms Cardiovascular Diseases
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An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.

Sensors (Basel, Switzerland) Jun 3, 2024
In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. ...
Humans Neural Networks, Computer Algorithms Female Signal Processing, Computer-Assisted Young Adult Fuzzy Logic Adult Lie Detection Male Electroencephalography
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Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.

IEEE transactions on neural networks and learning systems Jun 3, 2024
The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural information between the columns or rows of feature matrices. Due to the intersubject variability of EE...
Humans Least-Squares Analysis Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Machine Learning Algorithms
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An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

IEEE transactions on neural networks and learning systems Jun 3, 2024
Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explaina...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Brain Humans Deep Learning Neural Networks, Computer Electroencephalography Algorithms
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GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

IEEE transactions on neural networks and learning systems Jun 3, 2024
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the ...
Neural Networks, Computer Algorithms Brain Humans Signal Processing, Computer-Assisted Adult Deep Learning Brain-Computer Interfaces Electroencephalography Imagination
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Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jun 3, 2024
Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-sourc...
Movement Male Walking Female Humans Muscle, Skeletal Deep Learning Gait Young Adult Neural Networks, Computer Signal Processing, Computer-Assisted Adult Intention Lower Extremity Algorithms Electromyography
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Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

Biomedical engineering online Jun 1, 2024
BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated ...
Automation Deep Learning Time Factors Child Electroencephalography Humans Epilepsy Neural Networks, Computer Signal Processing, Computer-Assisted Diagnosis, Computer-Assisted
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Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals.

Scientific reports Jun 1, 2024
Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intr...
Pregnancy Female Deep Learning Humans Neural Networks, Computer Fetus Fetal Monitoring Cardiotocography Signal Processing, Computer-Assisted Heart Rate, Fetal
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Seizure Detection Based on Lightweight Inverted Residual Attention Network.

International journal of neural systems May 31, 2024
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detectio...
Humans Attention Electroencephalography Neural Networks, Computer Seizures Signal Processing, Computer-Assisted
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