# RespNet: A deep learning model for extraction of respiration from photoplethysmogram

## TL;DR

This paper introduces RespNet, a deep learning model that extracts respiration signals from PPG data, enabling continuous, unobtrusive respiratory monitoring using wearable devices, with improved accuracy over traditional methods.

## Contribution

The paper presents a novel end-to-end deep learning network, RespNet, for extracting respiration signals from PPG, outperforming conventional signal processing approaches.

## Key findings

- Mean Squared Error of 0.262 and 0.145 on two datasets
- Cross-Correlation coefficients of 0.933 and 0.931
- Better performance than traditional methods

## Abstract

Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is seldom used outside the intensive care ward due to the large size and cost of the monitoring system. While Electrocardiogram (ECG) based respiration extraction is a validated approach, its adoption is limited by access to a suitable continuous ECG monitor. Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring. Research in this domain, however, has been predominantly focussed on estimating respiration rate from PPG. In this work, a novel end-to-end deep learning network called RespNet is proposed to perform the task of extracting the respiration signal from a given input PPG as opposed to extracting respiration rate. The proposed network was trained and tested on two different datasets utilizing different modalities of reference respiration signal recordings. Also, the similarity and performance of the proposed network against two conventional signal processing approaches for extracting respiration signal were studied. The proposed method was tested on two independent datasets with a Mean Squared Error of 0.262 and 0.145. The Cross-Correlation coefficient of the respective datasets were found to be 0.933 and 0.931. The reported errors and similarity was found to be better than conventional approaches. The proposed approach would aid clinicians to provide comprehensive evaluation of sleep-related respiratory conditions and chronic respiratory ailments while being comfortable and inexpensive for the patient.