RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation

TL;DR

This paper introduces RRWaveNet, a compact deep learning model that accurately estimates respiratory rate from raw PPG signals, enabling continuous, remote monitoring with low-cost devices and demonstrating improved performance through transfer learning.

Contribution

The study presents a novel end-to-end CNN model for RR estimation that does not require feature engineering and leverages transfer learning for enhanced accuracy in remote settings.

Findings

RRWaveNet outperforms state-of-the-art methods in multiple datasets.

Transfer learning reduces MAE by up to 21% in remote monitoring scenarios.

Model enables practical RR estimation on affordable wearable devices.

Abstract

Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in four datasets (BIDMC, CapnoBase, WESAD, and SensAI) and using three window sizes (16, 32, and 64 seconds). RRWaveNet outperformed current state-of-the-art methods with mean absolute errors at optimal window size of 1.66 \pm 1.01, 1.59 \pm 1.08, 1.92 \pm 0.96 and 1.23 \pm 0.61 breaths per minute for each dataset. In remote monitoring settings, such as in the WESAD and SensAI datasets, we apply transfer learning to improve the performance using two other ICU datasets as pretraining datasets, reducing the MAE by up to 21$\%$. This shows that this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study also shows feasibility of remote RR monitoring in the context of telemedicine and at home.