AIMC Topic: Photoplethysmography

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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

Scientific reports Jul 2, 2025
In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block of life and constitutes the significant share of energy produced by the fetus and the neonate during...
Humans Algorithms Female Photoplethysmography Monitoring, Physiologic Hypoglycemia Infant, Premature Infant, Newborn Deep Learning Blood Glucose
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Channel attention pyramid network for remote physiological measurement.

Scientific reports Jul 2, 2025
Remote photoplethysmography (rPPG) is an emerging contactless physiological parameter detection method utilizing cameras, showing great promise as a forefront technology for remote health assessment. While traditional rPPG methods have substantially ...
Humans Video Recording Deep Learning Photoplethysmography Monitoring, Physiologic
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Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Scientific reports Jul 1, 2025
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...
Blood Pressure Determination Blood Pressure Signal Processing, Computer-Assisted Female Male Humans Middle Aged Neural Networks, Computer Deep Learning Wearable Electronic Devices Photoplethysmography
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Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.

Computer methods and programs in biomedicine Apr 20, 2025
BACKGROUND: It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias. We assessed, whether a neural network-based classifier can distinguish the origin of...
Adult Male Middle Aged Female Aged Young Adult Signal Processing, Computer-Assisted Heart Ventricles Tachycardia, Supraventricular Adolescent Photoplethysmography Electrocardiography Humans Neural Networks, Computer Machine Learning
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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Biosensors Apr 16, 2025
Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent compli...
Diabetes Mellitus Blood Glucose Self-Monitoring Federated Learning Blood Glucose Biosensing Techniques Photoplethysmography Humans Neural Networks, Computer Machine Learning
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Parallel convolutional neural networks for non-invasive cardiac hemodynamic estimation: integrating uncalibrated PPG signals with nonlinear feature analysis.

Physiological measurement Mar 31, 2025
Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health assessment, as it is governed by key parameters such as cardiac output (CO), systemic vascular resistance (SVR), and arterial compliance (AC). This study a...
Signal Processing, Computer-Assisted Heart Nonlinear Dynamics Adult Male Middle Aged Hemodynamics Humans Deep Learning Neural Networks, Computer Convolutional Neural Networks Female Photoplethysmography
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Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

Biosensors Mar 24, 2025
The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO is a powerf...
Wearable Electronic Devices Photoplethysmography Oxygen Consumption Female Adult Male Humans Machine Learning Algorithms
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Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.

Scientific reports Mar 12, 2025
Every year, Coronary Artery Disease (CAD) claims lives of over a million people. CAD occurs when the coronary arteries, responsible for supplying oxygenated blood to the heart, get occluded due to plaque deposits on their inner walls. The most critic...
Humans Machine Learning Female Aged Male Middle Aged Coronary Artery Disease Early Diagnosis Arterial Pressure Photoplethysmography Blood Pressure Signal Processing, Computer-Assisted
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MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Computers in biology and medicine Mar 1, 2025
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...
Humans Neural Networks, Computer Databases, Factual Signal Processing, Computer-Assisted Atrial Fibrillation Photoplethysmography Electrocardiography
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Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning.

Computer methods and programs in biomedicine Feb 27, 2025
BACKGROUND: With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-...
Photoplethysmography Signal Processing, Computer-Assisted Atrial Fibrillation Databases, Factual Deep Learning Humans Wearable Electronic Devices Machine Learning Algorithms
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