AIMC Topic: Signal Processing, Computer-Assisted

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Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

Medical & biological engineering & computing May 9, 2024
Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannia...
Imagination Algorithms Brain Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans
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Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.

Sensors (Basel, Switzerland) May 9, 2024
Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the po...
Human Activities Galvanic Skin Response Photoplethysmography Algorithms Signal Processing, Computer-Assisted Female Accelerometry Young Adult Humans Adult Machine Learning Male Wearable Electronic Devices
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Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach.

Biomedical physics & engineering express May 9, 2024
Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an ...
Humans Neural Networks, Computer Deep Learning Electrocardiography Signal Processing, Computer-Assisted Machine Learning Algorithms Databases, Factual
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The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG).

Biomedical physics & engineering express May 7, 2024
Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recor...
Magnetocardiography Heart Heart Rate Myocardial Ischemia Adult Humans Case-Control Studies Female Male Electrocardiography Machine Learning Middle Aged Signal Processing, Computer-Assisted Algorithms Reproducibility of Results Aged
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Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening.

IEEE journal of translational engineering in health and medicine May 7, 2024
OBJECTIVE: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the...
Signal Processing, Computer-Assisted Atrial Fibrillation Tachycardia, Supraventricular Humans Electrocardiography Support Vector Machine
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Combining enhanced spectral resolution of EMG and a deep learning approach for knee pathology diagnosis.

PloS one May 7, 2024
Knee osteoarthritis (OA) is a prevalent, debilitating joint condition primarily affecting the elderly. This investigation aims to develop an electromyography (EMG)-based method for diagnosing knee pathologies. EMG signals of the muscles surrounding t...
Signal Processing, Computer-Assisted Humans Electromyography Algorithms Knee Joint Aged Female Middle Aged Deep Learning Adult Muscle, Skeletal Osteoarthritis, Knee Male
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Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Medical & biological engineering & computing May 6, 2024
Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rat...
Algorithms Adult Male Signal Processing, Computer-Assisted Humans Heart Rate Neural Networks, Computer Fatigue Exercise Electrocardiography Support Vector Machine
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Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Computers in biology and medicine May 6, 2024
Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models...
Humans Neural Networks, Computer Deep Learning Electrocardiography Signal Processing, Computer-Assisted Knowledge Discovery
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Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.

Sensors (Basel, Switzerland) May 6, 2024
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturb...
Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Electrocardiography Humans
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Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

Sensors (Basel, Switzerland) May 6, 2024
The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FE...
Fetus Fetal Monitoring Deep Learning Electrocardiography Humans Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Pregnancy Female
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