Key result
A proposed 1D CNN method for PPG signal quality assessment achieved 95.63% accuracy for heart rate and outperformed 2D CNN approaches and other state-of-the-art algorithms.
Why the study?
Existing PPG signal quality assessment methods focus on morphological features suitable for HR detection but do not reliably apply to HRV.
A novel 1D CNN deep learning approach accurately assesses PPG signal quality for reliable extraction of heart rate and heart rate variability parameters in home-based monitoring.
New PPG artifact removal methods show technical promise; leaves open real-world impact on ambulatory vital sign accuracy.
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 corresponding electrocardiogram signal, using 210 hours of data collected from a home-based health monitoring application. Our results show that the proposed 1D CNN method outperforms the other 2D CNN approaches. Our 1D CNN model obtains the accuracy of 95.63%, 96.71%, 91.42%, 94.01%, and 94.81% for the HR, average of normal to normal interbeat (NN) intervals, root mean square of successive NN interval differences, standard deviation of NN intervals, and ratio of absolute power in low frequency to absolute power in high frequency ratios, respectively. Moreover, we compare the performance of our proposed method with state-of-the-art algorithms. We compare our best models for HR-HRV health parameters with six different state-of-the-art PPG signal quality assessment methods. Our results indicate that the proposed method performs better than the other methods. We also provide the open source model implemented in Python for the community to be integrated into their solutions.
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Naeini et al. (2023) studied Photoplethysmography (PPG) signal quality assessment. 1D Convolutional Neural Network (CNN) vs. 2D CNN approaches and state-of-the-art algorithms was evaluated on Accuracy for heart rate (HR) detection. A proposed 1D CNN method for PPG signal quality assessment achieved 95.63% accuracy for heart rate and outperformed 2D CNN approaches and other state-of-the-art algorithms.
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