AIMC Topic: Photoplethysmography

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model.

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 this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multim...
Female Adult Stress, Psychological Photoplethysmography Male Humans Neural Networks, Computer Signal Processing, Computer-Assisted Electrocardiography Automobile Driving Support Vector Machine Respiratory Rate
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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 ...
Signal Processing, Computer-Assisted Blood Pressure Determination Blood Pressure Humans Machine Learning Reproducibility of Results Algorithms Photoplethysmography
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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics.

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
Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wav...
Vascular Stiffness Pulse Wave Analysis Signal Processing, Computer-Assisted Deep Learning Photoplethysmography Algorithms Female Male Cardiovascular Diseases Adult Humans
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Heart Rate Estimation from Neck Photoplethysmography using FFT-Based Scoring and a Shallow Neural Network.

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
Heart rate (HR) is one of the most important vital signs to monitor. Constantly monitoring HR makes it important to have an easy-to-use system that is able to achieve clinically acceptable measurement accuracy. Depending on the monitoring device's ul...
Algorithms Female Adult Humans Neural Networks, Computer Heart Rate Neck Signal Processing, Computer-Assisted Male Photoplethysmography
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A Novel Machine-Learning-Based Noise Detection Method for Photoplethysmography 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
Wearable devices are widespread for continuous health monitoring; capturing various physiological parameters for remote health monitoring and early detection of health issues. These devices are susceptible to interference such as Motion Artifacts (MA...
Artifacts Signal-To-Noise Ratio Photoplethysmography Signal Processing, Computer-Assisted Humans Algorithms Machine Learning Wearable Electronic Devices
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A CNN and Transformer Hybrid Network for Multi-Class Arrhythmia Detection from Photoplethysmography.

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
Photoplethysmography (PPG)-based arrhythmia detection methods have gained attention with wearable technology, enabling early detection of undiagnosed arrhythmias. Existing methods excel in single arrhythmia detection but struggle with multiple arrhyt...
Humans Photoplethysmography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Neural Networks, Computer Algorithms
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Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel 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, 2023
In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity...
Electrocardiography Support Vector Machine Respiratory Rate Machine Learning Photoplethysmography
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Risk assessment of diabetic retinopathy with machine and deep learning models with PPG signals and PWV.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can ...
Risk Assessment Diabetes Mellitus Deep Learning Pulse Wave Analysis Photoplethysmography Humans Diabetic Retinopathy
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[A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Jun 25, 2023
Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed ...
Machine Learning Neural Networks, Computer Photoplethysmography
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[Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation Jan 30, 2023
OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.
Heart Rate Photoplethysmography Neural Networks, Computer Anesthesia Electrocardiography Artificial Intelligence
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