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Interbeat interval-based sleep staging: work in progress toward real-time implementation.
Physiol Meas
March 2022
Sleep Number Labs, San Jose, CA, United States of America.
. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals.
View Article and Find Full Text PDFRecent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey.
Sensors (Basel)
May 2021
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life.
View Article and Find Full Text PDFiPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox.
Annu Int Conf IEEE Eng Med Biol Soc
July 2019
Imaging-based, non-contact measurement of physiology (including imaging photoplethysmography and imaging ballistocardiography) is a growing field of research. There are several strengths of imaging methods that make them attractive. They remove the need for uncomfortable contact sensors and can enable spatial and concomitant measurement from a single sensor.
View Article and Find Full Text PDFWearable ballistocardiogram and seismocardiogram systems for health and performance.
J Appl Physiol (1985)
February 2018
School of Electrical and Computer Engineering and Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia.
Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments.
View Article and Find Full Text PDFBallistocardiographic artifact removal from simultaneous EEG-fMRI using an optical motion-tracking system.
Neuroimage
July 2013
Dept. of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany.
The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allows the investigation of neuronal activity with high temporal and spatial resolution. While much progress has been made to overcome the multiple technical challenges associated with the recording of EEG inside the MR scanner, the ballistocardiographic (BCG) artifact, which is caused by cardiac-related motion inside the magnetic field, remains a major issue affecting EEG quality. The BCG is difficult to remove by standard average artifact subtraction (AAS) methods due to its variability across cardiac cycles.
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