AIMC Topic: Signal Processing, Computer-Assisted

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A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the qua...
Signal Processing, Computer-Assisted Algorithms Humans Electromyography Neural Networks, Computer Reproducibility of Results Electrocardiography Signal-To-Noise Ratio
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EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and Physiological Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Understanding the emotional dynamics within social interactions is crucial for meaningful interpretation. Despite progress in emotion recognition systems, recognizing the collective emotional climate among peers has been understudied. Addressing this...
Signal Processing, Computer-Assisted Heart Rate Peer Group Speech Humans Emotions Deep Learning Galvanic Skin Response
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Complexity Analysis based on Parietal Fuzzy Entropy to Facilitate ADHD Diagnosis in Young Children.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and accurate diagnosis of ADHD in young children is of paramount importance. In this study, the 8-channel ...
Entropy Attention Deficit Disorder with Hyperactivity Fuzzy Logic Female Electroencephalography Signal Processing, Computer-Assisted Male Humans Child Child, Preschool Support Vector Machine
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An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing r...
Female Male Humans Electroencephalography Stroke Rehabilitation Deep Learning Signal Processing, Computer-Assisted Robotics Stroke Movement
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TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatment and recovery. With the rapid development of deep learning, CNN, LSTM, and attention-based models have been used for auxiliary diagnosis of MDD fro...
Signal Processing, Computer-Assisted Depression Electroencephalography Humans Algorithms Neural Networks, Computer Depressive Disorder, Major
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Challenging Deep Learning Methods for EEG Signal Denoising under Data Corruption.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Capturing informative electroencephalogram (EEG) signals is a challenging task due to the presence of noise (e.g., due to human movement). In extreme cases, data recordings from specific electrodes (channels) can become corrupted and entirely devoid ...
Deep Learning Electroencephalography Signal-To-Noise Ratio Signal Processing, Computer-Assisted Humans Algorithms
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EEG Acquisition and Motor Imagery Classification for Robotic Control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating th...
Robotics Electroencephalography Signal Processing, Computer-Assisted Machine Learning Support Vector Machine Imagination Brain-Computer Interfaces Movement Humans
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Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted ...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Radar Algorithms Deep Learning
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Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features ...
Signal Processing, Computer-Assisted Rest Machine Learning Unsupervised Machine Learning Algorithms Electroencephalography Brain Humans Deep Learning Cluster Analysis
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ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electrocardiogram data provide a tremendous opportunity for the detection of various types of cardiac arrhythmia. Recent advancement in ubiquitous wearable devices with incorporated ECG sensors offers an opportunity for a real-time monitoring system ...
Supervised Machine Learning Humans Electrocardiography Arrhythmias, Cardiac Algorithms Signal Processing, Computer-Assisted Atrial Fibrillation
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