A Deep Learning–based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability

作者：Emad Kasaeyan Naeini, Fatemeh Sarhaddi, I. Azimi, P. Liljeberg, N. Dutt, A. Rahmani · 发表于：ACM Trans. Comput. Heal. · 年份：2023 · DOI：10.1145/3616019 · 被引用次数：40 · 研究领域：Computer Science

Photoplethysmography (PPG) is a non-invasive optical method to acquire various vital signs, including heart rate (HR) and heart rate variability (HRV). The PPG method is highly susceptible to motion artifacts and environmental noise. Unfortunately, such artifacts are inevitable in ubiquitous health monitoring, as the users are involved in various activities in their daily routines. Such low-quality PPG signals negatively impact the accuracy of the extracted health parameters, leading to inaccurate decision-making. PPG-based health monitoring necessitates a quality assessment approach to determine the signal quality according to the accuracy of the health parameters. Different studies have thus far introduced PPG signal quality assessment methods, exploiting various indicators and machine learning algorithms. These methods differentiate reliable and unreliable signals, considering morphological features of the PPG signal and focusing on the cardiac cycles. Therefore, they can be utilized for HR detection applications. However, they do not apply to HRV, as only having an acceptable shape is insufficient, and other signal factors may also affect the accuracy. In this article, we propose a deep learning–based PPG quality assessment method for HR and various HRV parameters. We employ one customized one-dimensional (1D) and three 2D Convolutional Neural Networks (CNN) to train models for each parameter. Reliability of each of these parameters will be evaluated against the correspon...