Photoplethysmography based atrial fibrillation detection: a continually growing field

Area of Science:

• Biomedical Engineering
• Digital Health
• Artificial Intelligence

Background:

• Atrial fibrillation (AF) is a common arrhythmia linked to serious health issues like stroke and increased mortality.
• Photoplethysmography (PPG) is a cost-effective technology for continuous AF monitoring, integrated into wearable devices.
• Previous reviews on PPG-based AF detection concluded before June 2019, necessitating an update on recent advancements.

Purpose of the Study:

• To provide a comprehensive review of recent developments in PPG-based AF detection from July 2019 to December 2022.
• To analyze the application of digital health and AI solutions in this field.
• To identify and assess 57 relevant studies published within the specified timeframe.

Main Methods:

• Systematic literature search across scientific databases.
• In-depth assessment of statistical methodologies, traditional machine learning, and deep learning approaches.
• Analysis of challenges and limitations in current PPG-based AF detection techniques.

Main Results:

• Identification of 57 pertinent studies on PPG-based AF detection published between July 2019 and December 2022.
• Evaluation of diverse AI and digital health strategies applied to PPG signals for AF detection.
• Summary of common statistical, machine learning, and deep learning techniques used.

Conclusions:

• Significant progress has been made in PPG-based AF detection using AI and digital health since 2019.
• Continued research is needed to address existing challenges and refine detection methodologies.
• A dedicated website is maintained to track the latest research and updates in this rapidly evolving field.