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In the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning.

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Purpose: Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored.

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Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review.

Sensors (Basel)

November 2024

Programa de Mecatrónica, Facultad de Ingeniería, Universidad Autónoma de Occidente, Calle 25 # 115-85 Vía Cali-Jamundí, Santiago de Cali 760030, Colombia.

Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals.

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Introduction Photoplethysmography (PPG) measures are important in monitoring peripheral oxygen saturation (SpO). Another parameter is a derived quantity referred to as the peripheral perfusion index (PPI). It is calculated as the ratio of the peak-to-peak pulse amplitude of a PPG signal (PA) to the non-pulsating part of the total PPG signal.

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Introduction: COVID-19 is a global health issue that can affect the cardiovascular system at various levels and cause damage to the endothelium, leading to decreased arterial elasticity and vascular dysfunction. This study investigates the relationship between COVID-19 history and arterial vascular elasticity in medical students.

Methods: This study used a case-control design and a purposive random sampling technique.

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