Detection of atrial fibrillation using photoplethysmography signals: a systemic review

Cheuk-To Skylar Chung, Vellaisamy Roy, Gary Tse, Haipeng Liu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The rising prevalence of atrial fibrillation (AF) continues to impose a burden on the healthcare systems globally, leading to significant economic losses. Currently, electrocardiography (ECG) is the golden standard for AF detection. However, ECG recordings may be misinterpreted as other conditions such as sinus tachycardia or supraventricular tachycardia. This notwithstanding, the high cost, limited portability, and short duration of ECG recordings can pose additional limitations to its use. The recent development of AF detection technology using photoplethysmography (PPG) signals yields promising potential. Subsequently, the clinical implications of AF detection using wearable technology can enable the early detection and timely management of AF, thereby reducing patient morbidity and mortality. However, the accuracy of PPG-based AF detection can be limited by some technical issues. Current guidelines are restricted to ECG-based methods. Therefore, the aim of this chapter is to perform a systemic review of the existing literature on AF detection using PPG signals. Overall, the available evidence reveals that PPG is an effective, user-friendly, low-cost method for long-term screening of AF. Nonetheless, further prospective studies need to be conducted to compare the performance of current AF detection methods versus PPG-based approaches.
Original languageEnglish
Title of host publicationSignal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing
EditorsRajesh Kumar Tripathy, Ram Bilas Pachori
PublisherAcademic Press
Chapter4
Pages49-63
Number of pages15
Edition1
ISBN (Electronic)9780443141409
ISBN (Print)9780443141416
DOIs
Publication statusPublished - 12 Jun 2024

Keywords

  • wearable technology
  • long-term screening
  • heart disease
  • arrhythmia
  • healthcare policy
  • artificial intelligence
  • PPG

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