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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
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...

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
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 ...

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
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...

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
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...

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
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...

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
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...

A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.

Sensors (Basel, Switzerland)
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), ...

[A review of deep learning methods for non-contact heart rate measurement based on facial videos].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring...

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Computers in biology and medicine
Data fusion, involving the simultaneous integration of signals from multiple sensors, is an emerging field that facilitates more accurate inferences in instrumentation applications. This paper presents a novel fusion methodology for multi-sensor mult...

External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial f...