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Heart Rate

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Application of photoplethysmography signals for healthcare systems: An in-depth review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, ...

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while ...

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals.

Journal of healthcare engineering
As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-b...

Application research of pulse signal physiology and pathology feature mining in the field of disease diagnosis.

Computer methods in biomechanics and biomedical engineering
This experiment is based on the principle of traditional Chinese medicine (TCM) pulse diagnosis, the human pulse signal collected by the sensor is organized into a dataset, and the algorithms are designed to apply feature extraction. After denoising,...

Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction.

Scientific reports
This study looked at novel data sources for cardiovascular risk prediction including detailed lifestyle questionnaire and continuous blood pressure monitoring, using ensemble machine learning algorithms (MLAs). The reference conventional risk score c...

Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.

IEEE journal of biomedical and health informatics
This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from...

Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Scientific reports
Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (...

Accelerated cardiac T mapping in four heartbeats with inline MyoMapNet: a deep learning-based T estimation approach.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PURPOSE: To develop and evaluate MyoMapNet, a rapid myocardial T mapping approach that uses fully connected neural networks (FCNN) to estimate T values from four T-weighted images collected after a single inversion pulse in four heartbeats (Look-Lock...

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Sensors (Basel, Switzerland)
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlyin...

Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform.

PloS one
Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods...