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

TL;DR

This paper introduces a novel CycleGAN-based method for removing motion artifacts from PPG signals without using accelerometers, achieving significantly better accuracy and lower power consumption than existing techniques.

Contribution

The paper presents a new non-accelerometer-based PPG artifact removal technique using CycleGAN, outperforming current methods in accuracy and power efficiency.

Findings

9.5 times improvement in artifact removal accuracy

No need for additional accelerometer sensors

Effective reconstruction of clean PPG signals from noisy data

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.