AIMC Topic: Photoplethysmography

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Non-invasive arterial blood pressure measurement and SpO estimation using PPG signal: a deep learning framework.

BMC medical informatics and decision making
BACKGROUND: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measureme...

A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography.

Computer methods and programs in biomedicine
CONTEXT: Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a P...

Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research.

Sensors (Basel, Switzerland)
In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG s...

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism.

Scientific reports
Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, maki...

A deep learning method for continuous noninvasive blood pressure monitoring using photoplethysmography.

Physiological measurement
. The aim of this study is to investigate continuous blood pressure waveform estimation from a plethysmography (PPG) signal, thus providing more human cardiovascular status information than traditional cuff-based methods.. The proposed method utilize...

Mental Stress Detection Using a Wearable In-Ear Plethysmography.

Biosensors
This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of m...

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography.

IEEE journal of biomedical and health informatics
Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. Sleep is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to p...

X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation.

Computers in biology and medicine
Pulse rate (PR) is one of the most important markers for assessing a person's health. With the increasing demand for long-term health monitoring, much attention is being paid to contactless PR estimation using imaging photoplethysmography (iPPG). Thi...

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.

IEEE transactions on bio-medical engineering
Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation s...

Deep learning-based remote-photoplethysmography measurement from short-time facial video.

Physiological measurement
. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manu...