title: Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach.
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.
authors.affiliation: Center of Excellence in Telecommunication Applications , King Abdulaziz City for Science and Technology , Riyadh , Saudi Arabia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Alqaraawi
authors.affiliation: Center of Excellence in Telecommunication Applications , King Abdulaziz City for Science and Technology , Riyadh , Saudi Arabia.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Alwosheel
authors.affiliation: Center of Excellence in Telecommunication Applications , King Abdulaziz City for Science and Technology , Riyadh , Saudi Arabia.
authors.forename: Amr
authors.initials: A
authors.lastname: Alasaad
keywords: Bayes methods; Bayesian learning approach; PPG sensors; automatic multiscale-based peak detection algorithm; cardiology; heart activities; heart rate variability estimation; learning (artificial intelligence); medical signal detection; motion artefact; photoplethysmography; photoplethysmography signals; probabilistic approach; smart phones; smart watches; wearable computers; wearable devices