AIMC Topic: Photoplethysmography

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Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers.

Physiological measurement
Objective.Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, ...

Assessment of Blood Glucose Measurement Using New Noninvasive Technology: Protocol and Methodology.

JMIR research protocols
BACKGROUND: Diabetes mellitus (DM) is a major noncommunicable disease with a significant increase in prevalence, especially in low- and middle-income countries. The latest International Diabetes Federation Diabetes Atlas (2025) reports that 11.1% of ...

Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.

Scientific reports
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit us...

Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities.

Biomedical physics & engineering express
Photoplethysmography (PPG) is widely used in wearable health monitors for tracking fundamental physiological parameters (e.g., heart rate and blood oxygen saturation) and advancing applications requiring high-quality signals-such as blood pressure as...

SleepPPG-Net2: deep learning generalization for sleep staging from photoplethysmography.

Physiological measurement
. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle...

A multimodal physiological dataset for non-invasive blood glucose estimation.

Scientific data
Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs)...

Multifunctional electronic skin integrating dual-mode optical and pressure sensors for caregiving robots.

Nanoscale horizons
Advancements in artificial intelligence have broadened the capabilities of robots, particularly in caregiving applications that are essential for aging societies facing a growing shortage of human caregivers. Humanoid caregiving robots require sophis...

Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.

Scientific reports
Monitoring mean arterial pressure (MAP) is essential for ensuring safe general anesthesia. Current practices rely either on non-invasive cuff measurements, which suffer from poor temporal resolution, or invasive arterial lines, which provide excellen...

Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signals.

Scientific reports
Pulse Wave Velocity (PWV) is a widely recognized non-invasive biomarker of arterial stiffness and an independent predictor of cardiovascular risk, including atherosclerosis, hypertension, and vascular aging. Accurate, accessible estimation of PWV is,...

Benchmarking of open-source algorithms for heart rate estimation from motion-corrupted photoplethysmography.

Computers in biology and medicine
Photoplethysmography holds promise for continuous, non-intrusive heart rate monitoring through wearable devices. However, motion artifacts can impact the reliability of heart rate estimates. The integration of accelerometer data has been proven helpf...