AIMC Topic: Photoplethysmography

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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.

Scientific reports
Driver drowsiness is a critical issue in transportation systems and a leading cause of traffic accidents. Common factors contributing to accidents include intoxicated driving, fatigue, and sleep deprivation. Drowsiness significantly impairs a driver'...

Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models.

Journal of medical systems
Traditional cuffless blood pressure (BP) estimation methods often require collecting physiological signals, such as electrocardiogram (ECG) and photoplethysmography (PPG), from two distinct body sites to compute metrics like pulse transit time (PTT) ...

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

Scientific reports
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...

Channel attention pyramid network for remote physiological measurement.

Scientific reports
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 ...

Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Scientific reports
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-...

Multi-datasets transfer multitask learning for simultaneous blood glucose and blood pressure monitoring using common PPG features.

Computers in biology and medicine
The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of either task. However, the lack of availab...

Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.

Computer methods and programs in biomedicine
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...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Biosensors
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...

Classification of anemic condition based on photoplethysmography signals and clinical dataset.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: One of the worldwide public health issues mostly affecting children and expectant mothers is Anemia. Recently, non-invasive hemoglobin (Hb) measurements, such as machine learning (ML) algorithms, can diagnose Anemia more quickly and effic...

Parallel convolutional neural networks for non-invasive cardiac hemodynamic estimation: integrating uncalibrated PPG signals with nonlinear feature analysis.

Physiological measurement
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...