Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach. Issue 2 (13th June 2016)

• Record Type: Journal Article
• Authors: Alqaraawi, Ahmed; Alwosheel, Ahmad; Alasaad, Amr
• Is Part Of: Healthcare technology letters. Volume 3:Issue 2(2016)
• Page Start: 136
• Page End: 142
• Publication Date: 2016-06-13
• Subjects: photoplethysmography -- cardiology -- Bayes methods -- learning (artificial intelligence) -- wearable computers -- medical signal detection; heart rate variability estimation -- photoplethysmography signals -- Bayesian learning approach -- PPG sensors -- wearable devices -- smart watches -- smart phones -- heart activities -- motion artefact -- probabilistic approach -- automatic multiscale‐based peak detection algorithm; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2016.0006
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : Heart rate variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. However, PPG signal is very sensitive to motion artefact which may lead to poor HRV estimation if false peaks are detected. In this Letter, the authors propose a probabilistic approach based on Bayesian learning to better estimate HRV from PPG signal recorded by wearable devices and enhance the performance of the automatic multi scale‐based peak detection (AMPD) algorithm used for peak detection. The authors' experiments show that their approach enhances the performance of the AMPD algorithm in terms of number of HRV related metrics such as sensitivity, positive predictive value, and average temporal resolution.