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Heart Rate

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Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach.

Sensors (Basel, Switzerland)
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...

Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.

Scientific reports
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 ...

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study.

Scientific reports
Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patie...

Detecting fine and elaborate movements with piezo sensors provides non-invasive access to overlooked behavioral components.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Behavioral phenotyping devices have been successfully used to build ethograms, but many aspects of behavior remain out of reach of available phenotyping systems. We now report on a novel device, which consists in an open-field platform resting on hig...

Advanced computation in cardiovascular physiology: new challenges and opportunities.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While th...

Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation.

Sensors (Basel, Switzerland)
Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an ...

Model Construction of Using Physiological Signals to Detect Mental Health Status.

Journal of healthcare engineering
BACKGROUND: Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health.

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.

Computational intelligence and neuroscience
The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning tech...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BMC cardiovascular disorders
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...

Cardiorespiratory and metabolic demand of the 6-minute pegboard and ring test in healthy young adults.

Journal of bodywork and movement therapies
OBJECTIVE: To determine the cardiorespiratory and metabolic demand of the Six-Minute Pegboard and Ring Test (6PBRT) in healthy young adults and its association with maximal arm cycle ergometer test (arm CET).