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Related Concept Videos

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...

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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and

Hong Wang1,2, Binbin Liu1,2, Hui Zhang1

  • 1Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, 6 Fucheng Road, Haidian District, Beijing, 100048, China, +86-10-66957703.

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|November 10, 2025
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A smartwatch photoplethysmography (PPG) model accurately tracks atrial fibrillation (AF) burden, showing high concordance with Holter monitoring for AF duration and variability. This offers new insights into AF progression dynamics.

Keywords:
arrhythmiaatrial fibrillationatrial fibrillation burdenphotoplethysmographywearable devices

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Digital Health

Background:

  • Atrial fibrillation (AF) burden is a key indicator for cardiovascular events like stroke and heart failure.
  • Photoplethysmography (PPG) technology offers a noninvasive method for detecting AF burden.

Purpose of the Study:

  • To develop and validate an AF burden model using smartwatch-derived PPG signals.
  • To assess the model's ability to track the progression of AF.

Main Methods:

  • A prospective pilot study involving 145 patients with paroxysmal AF.
  • Simultaneous monitoring using smartwatch PPG and 24-hour Holter ECG (gold standard).
  • Defined five PPG-derived AF burden metrics and evaluated algorithm performance using sensitivity, specificity, accuracy, precision, and F1 score.

Main Results:

  • The PPG-based AF burden model achieved high accuracy (93.3%) and F1 score (90.5%) compared to Holter monitoring.
  • Strong correlations were observed between PPG-derived AF episode duration (rs=0.8788) and variability (rs=0.7876) and the gold standard.
  • The model demonstrated significant discriminatory power (AUC=89.5%).

Conclusions:

  • The PPG-based AF burden model shows high concordance with 24-hour Holter monitoring.
  • This technology provides a promising tool for monitoring AF progression noninvasively.
  • Enables new perspectives for understanding AF dynamics and management.