AIMC Topic: Photoplethysmography

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Biometric Signals Estimation Using Single Photon Camera and Deep Learning.

Sensors (Basel, Switzerland)
The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon avalanche di...

Fast body part segmentation and tracking of neonatal video data using deep learning.

Medical & biological engineering & computing
Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practi...

Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only.

Sensors (Basel, Switzerland)
Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in o...

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.

Sensors (Basel, Switzerland)
Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very val...

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images.

Sensors (Basel, Switzerland)
Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it re...

Photoplethysmographic-based automated sleep-wake classification using a support vector machine.

Physiological measurement
OBJECTIVE: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep-wake classification is multi-sensor-ba...

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology.

JMIR mHealth and uHealth
BACKGROUND: There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as r...

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques.

Artificial intelligence in medicine
Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement methods based on pulse transit time or multiple parameters require simultaneous electrocardiogram (ECG) a...

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.

IEEE transactions on biomedical circuits and systems
Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly su...

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Sensors (Basel, Switzerland)
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurem...