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Enhancement of Remote PPG and Heart Rate Estimation with Optimal Signal Quality Index


With the popularity of non-invasive vital signs detection,
remote photoplethysmography (rPPG) is drawing attention
in the community. Remote PPG, or rPPG signals are extracted
by a contactless manner that is more prone to artifacts than
PPG signals collected by wearable sensors. To develop a robust
and accurate system to estimate heart rate (HR) from rPPG
signals, we propose a novel real-time dynamic ROI tracking
algorithm that is applicable to slight motions and light changes.
Furthermore, we develop and include the signal quality index
(SQI) to improve the HR estimation accuracy. Studies have
developed optimal SQI for PPG signals but not rPPG signals,
we select and test six SQIs: Perfusion, Kurtosis, Skewness, Zerocrossing,
Entropy, and signal-to-noise ratio (SNR) on 124 rPPG
sessions from 30 participants wearing masks. Based on the mean
absolute error (MAE) of HR estimation, the optimal SQI is
selected and validated by Mann–Whitney U test (MWU). Lastly,
we show that the HR estimation accuracy is improved by 29%
after removing outliers decided by the optimal SQI, and the best
result achieves the MAE of 2.308 bpm.

Author: Jiyang Li, Korosh Vatanparvar, Li Zhu, Jilong Kuang, Alex Gao

Published: IEEE-EMBS International Conference on Biomedical and Health Informatics(BHI) and the Body Sensor Networks(BSN) Conferences (IEEE BHI & BSN)

Date: Sep 27, 2022