AIMC Topic: Heart Rate

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

Journal of healthcare engineering Jan 27, 2022
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...
Deep Learning Heart Rate Signal Processing, Computer-Assisted Ballistocardiography Humans Algorithms
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Application research of pulse signal physiology and pathology feature mining in the field of disease diagnosis.

Computer methods in biomechanics and biomedical engineering Jan 21, 2022
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,...
Humans Algorithms Reproducibility of Results Support Vector Machine Heart Rate
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Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction.

Scientific reports Jan 20, 2022
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...
Heart Disease Risk Factors Life Style Blood Pressure Monitoring, Ambulatory Exercise Surveys and Questionnaires Hypertension Calcium Heart Rate Singapore Coronary Artery Disease Adult Humans Male Machine Learning Middle Aged Algorithms Female Aged
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Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.

IEEE journal of biomedical and health informatics Jan 17, 2022
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...
Humans Infant Heart Rate Infant, Premature Machine Learning Gestational Age Algorithms Infant, Newborn
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Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Scientific reports Jan 7, 2022
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 (...
Humans Prospective Studies Female Male Middle Aged Diagnosis, Computer-Assisted Deep Learning Reproducibility of Results Aged Aged, 80 and over Regional Blood Flow Atrial Fibrillation Heart Rate Pulsatile Flow Video Recording Electrocardiography Time Factors Face Predictive Value of Tests Image Processing, Computer-Assisted Skin Photoplethysmography
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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 Jan 6, 2022
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...
Predictive Value of Tests Heart Humans Heart Rate Reproducibility of Results Deep Learning Magnetic Resonance Imaging
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Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Sensors (Basel, Switzerland) Dec 22, 2021
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...
Time Factors Heart Rate Neural Networks, Computer Forecasting Deep Learning Humans
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Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform.

PloS one Dec 16, 2021
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...
Heart Rate Wavelet Analysis Databases, Factual Humans Electrocardiography Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms
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Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach.

Sensors (Basel, Switzerland) Dec 11, 2021
This study proposes a new index to measure the resilience of an individual to stress, based on the changes of specific physiological variables. These variables include electromyography, which is the muscle response, blood volume pulse, breathing rate...
Humans Biofeedback, Psychology Heart Rate Electromyography Machine Learning Principal Component Analysis
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Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.

Scientific reports Nov 23, 2021
Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes ...
Heart Rate Demography Young Adult Adult Humans Retrospective Studies Follow-Up Studies Male Middle Aged Registries Risk Factors Female Adolescent Area Under Curve Artificial Intelligence Aged Atrial Fibrillation Electrocardiography Aged, 80 and over Prognosis
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