An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

Abstract

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: i) high power consumption and ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.

VABAM: Variational Autoencoder for Amplitude-based Biosignal Augmentation within Morphological Identities

“…Augmentation models are essential for creating larger, more diverse training datasets, particularly for generating synthetic instances of rare conditions [26], [27]. Denoising models play a crucial role in removing various noise interferences from physiological signals, including baseline wander, muscle artifacts, and environmental noise [28], [29]. Modality transfers facilitate the integration of various signal types, such as converting PPG into ECG signals, which improves analysis and diagnostic capabilities [30].…”