
Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach
Author(s) -
Alqaraawi Ahmed,
Alwosheel Ahmad,
Alasaad Amr
Publication year - 2016
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2016.0006
Subject(s) - photoplethysmogram , computer science , heart rate variability , artificial intelligence , sensitivity (control systems) , wearable computer , wearable technology , bayesian probability , pattern recognition (psychology) , signal (programming language) , probabilistic logic , signal to noise ratio (imaging) , noise (video) , computer vision , heart rate , medicine , engineering , electronic engineering , telecommunications , programming language , filter (signal processing) , blood pressure , radiology , embedded system , image (mathematics)
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.