Sep 12, 2020
Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data.
Sep 10, 2018
This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband.
Jun 9, 2018
A wearable, non-ECG sensor will allow for rhythm monitoring for longer periods of time and therefore potentially provide the evidence to motivate future large studies on photoplethysmography (PPG) based AF detection.
Mar 4, 2018
We describe an evaluation of photoplethysmography (PPG) signals with two wavelengths channels (infrared and red) using a wrist-worn sensor for the estimation of heart rate variability (HRV) and oxygen saturation (SpO2)
Nov 13, 2017
Arterial pulsewaves recorded with a wearable biosensor and analyzed with machine learning algorithms could identify a signature of oHCM when compared to unaffected controls.
Nov 13, 2017
A wearable, non-ECG sensor will allow for rhythm monitoring for longer periods of time and therefore potentially provide the evidence to motivate future large studies on photoplethysmography (PPG) based AF detection.
Nov 13, 2017
This study is designed to assess (1) the accuracy of nocturnal biometric measurements collected by an investigational wrist-worn device, and (2) its utility as a portable screening device for detection of apnea.
Jun 20, 2017
This study aims to evaluate the utility of a wrist-worn device for detection of AF episodes as well as to study pulse wave changes and patient activity.
The trial’s primary objective is to determine, in a real-world setting, whether using wearable sensors in a risk-targeted screening population can diagnose asymptomatic AF more effectively than routine care. Additional key objectives include (1) exploring 2 rhythm-monitoring strategies-electrocardiogram-based and exploratory pulse wave-based-for detection of new AF, and (2) comparing long-term clinical and resource outcomes among groups.
We propose to extend the application of this widespread technology even in the medical environment. The aim of our work is to apply BT-ACTs for workflow management and patients’ performance monitoring in a Radiation Oncology Department. Indeed, in such medical department working on outpatients based regimen, workflow and clinical control may be complex
We conducted a respective study of our data and sought to evaluate the value of this technology in identifying atrial fibrillation (AF), resulting in changes to clinical management of the patient.
Feasibility and benchmarking in the clinical environment is a necessary first step before testing the prospective value of these remote monitored wearable-derived physiology measurements.
In the WATCH AF study, this algorithm is tested for the first time with PPG signals from a smartwatch (Samsung) and a wristband (Wavelet health). Our study aims to determine the accuracy of these applications compared to an ambulatory ECG system.
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