AIMC Topic: Machine Learning

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Machine Learning Approaches for Blood Pressure Classification from Photoplethysmogram: A Comparative Analysis.

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 cuffless estimation of blood pressure (BP) has become a prominent area of research in recent years fueled by its potential clinical implications and the growing interest from the wearable device industry. It has been accelerated by the emergence ...
Blood Pressure Determination Humans Algorithms Blood Pressure Machine Learning Reproducibility of Results Photoplethysmography Signal Processing, Computer-Assisted
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Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

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
Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenar...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Imagination Machine Learning Algorithms Electroencephalography
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Personalised Speech-Based PTSD Prediction Using Weighted-Instance 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
Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples' daily life and can affect their mental, physical, or soc...
Precision Medicine Female Adult Humans Stress Disorders, Post-Traumatic Machine Learning ROC Curve Male Speech
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Innovative Approaches to Gender Classification through Unsupervised Machine Learning and Multi-Activity Fusion.

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 the last decade, gender recognition has garnered significant attention for its diverse applications in healthcare, sports, rehabilitation, and wearable electronics. This study offers a wearable sensor device to record various activities using iner...
Machine Learning Algorithms Adult Male Female Humans Unsupervised Machine Learning Wearable Electronic Devices Cluster Analysis
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Simulating Accelerometer Signals of Parkinson's Gait Using Generative Adversarial Networks.

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
Wearable technologies have been demonstrated to have value in the objective assessment of Parkinson's disease. However, certain symptoms such as freezing of gait are challenging to monitor using current approaches. Data augmentation, wherein syntheti...
Accelerometry Generative Adversarial Networks Machine Learning Humans Algorithms Neural Networks, Computer Signal Processing, Computer-Assisted Parkinson Disease Gait
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Minimally invasive monitor of cardiac output based on the machine-learning analysis of the pulse contour of the peripheral arterial pressure.

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 the hemodynamic management of anesthetized patients during surgical operation, minimally invasive and accurate cardiac output (CO) monitoring is strongly required. We have developed a CO monitor based on the machine-learning analysis of the pulse ...
Blood Pressure Dogs Cardiac Output Arterial Pressure Machine Learning Animals Monitoring, Physiologic
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HRV-based Monitoring of Neonatal Seizures with Machine 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
With the rapid development of machine learning (ML) in biomedical signal processing, ML-based neonatal seizure detection using heart rate variability (HRV) parameters extracted from the electrocardiogram (ECG) has gained increasing interest. In this ...
Infant, Newborn Sensitivity and Specificity Male Machine Learning Female Monitoring, Physiologic Seizures Signal Processing, Computer-Assisted Electrocardiography Support Vector Machine Humans Heart Rate
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Cost-Saving Data-Driven Diabetic Retinopathy Prediction via a Sampling-Empowered Incremental Learning Approach.

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
Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to vision impairment or even blindness if not detected and treated in the early stage. Recently, leveraging the electronic health records (EHR) data, machine learning-based...
Cost Savings Diabetic Retinopathy Humans Machine Learning Electronic Health Records
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Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

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
Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently d...
Brain-Computer Interfaces Signal-To-Noise Ratio Electroencephalography Signal Processing, Computer-Assisted Humans Algorithms Machine Learning Deep Learning
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SimICL: A Simple Visual In-context Learning Framework for Ultrasound Segmentation.

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
Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction limits model generalizability. Visual in-context learning (ICL) is a n...
Humans Deep Learning Ultrasonography Image Processing, Computer-Assisted Machine Learning Algorithms
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