AIMC Topic: Photoplethysmography

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Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia.

PloS one
Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect ...

A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram.

Journal of healthcare engineering
The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) usi...

Remote photoplethysmography with constrained ICA using periodicity and chrominance constraints.

Biomedical engineering online
BACKGROUND: Remote photoplethysmography (rPPG) has been in the forefront recently for measuring cardiac pulse rates from live or recorded videos. It finds advantages in scenarios requiring remote monitoring, such as medicine and fitness, where contac...

A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques.

IEEE journal of biomedical and health informatics
Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study pr...

Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

Physiological measurement
This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse...

Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method.

Biomedical engineering online
BACKGROUND: Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term vari...

DeepPerfusion: A comprehensible two-branched deep learning architecture for high-precision blood volume pulse extraction based on imaging photoplethysmography.

Computers in biology and medicine
Imaging photoplethysmography (iPPG) is a contactless approach for the extraction of the blood volume pulsation (BVP). Analyzing the small intensity changes resulting from fluctuations in light absorption in upper skin layers enables BVP extraction. I...

Deep generative models for physiological signals: A systematic literature review.

Artificial intelligence in medicine
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG) and electromyogram (EMG). Compared to the existin...

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Computers in biology and medicine
Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not monitor it frequently enough to prevent serious complications. This is in part because the traditional cuff-based method is inconvenient, uncomfortabl...

A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The aim of this study is to extract a patient-specific viscosity equation from photoplethysmography (PPG) data. An aging society has increased the need for remote, non-invasive health monitoring systems. However, the circula...