Tri-Spectral PPG: Robust Reflective Photoplethysmography by Fusing Multiple Wavelengths for Cardiac Monitoring

TL;DR

This paper presents a novel learning-based method that fuses multi-channel PPG signals from different wavelengths into a single robust signal, improving heart rate estimation accuracy during real-world activities.

Contribution

It introduces a new signal fusion technique for multi-channel PPG that compensates for artifacts, enhancing cardiac monitoring in wearable devices.

Findings

Fusion PPG reduces heart rate estimation error by 23% compared to green PPG.

Method effectively mitigates motion and ambient light artifacts.

Evaluated on a novel 13-hour real-world dataset from 10 participants.

Abstract

Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whereas green is commonly used for metrics such as heart rate and heart rate variability, red and infrared are commonly used for pulse oximetry. In this paper, we introduce a novel method that simultaneously fuses multi-channel PPG signals into a single recovered PPG signal that can be input to further processing. Via signal fusion, our learning-based method compensates for the artifacts that affect wavelengths to different extents, such as motion and ambient light changes. We evaluate our method on a novel dataset of multi-channel PPG recordings and electrocardiogram recordings for reference from 10 participants over the course of 13 hours during real-world activities outside the laboratory. Using the fusion PPG signal our method recovered, participants' heart rates can be calculated with a mean error of 4.5\,bpm (23\% lower than from green PPG signals at 5.9\,bpm).