A New Framework to Estimate Breathing Rate From Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals

Ali Adami,Peter H Charlton,Faezeh Marzbanrad,Reza Boostani

doi:10.1109/access.2021.3066166

Abstract

Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical settings. However, it is still widely measured manually. In this paper, a novel framework is proposed to estimate the BR from an electrocardiogram (ECG), a photoplethysmogram (PPG), or a blood pressure (BP) signal. The framework uses Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods to extract respiratory signals, taking advantage of both time and frequency domain information. An Extended Kalman Filter (EKF), incorporating a Signal Quality Index (SQI), enabled our method to achieve acceptable performance even for significantly distorted periods of the signals. Using state vector fusion, the output signals are combined and finally the BR is estimated. The framework was tested on two publicly available clinical databases: the MIT-BIH Polysomnographic and BIDMC databases. Performance was evaluated using the mean absolute percentage error (MAPE). The results indicated high accuracy: MAPEs on the two databases of 3.9% and 3.6% for ECG signals, 6.0% for PPG, and 5.0% for BP signals. The results also indicated high robustness to noise down to 0dB. Therefore, this framework may have utility for BR monitoring in high noise settings.

Highlights

Breathing Rate (BR) is a valuable physiological marker measured from patients in a wide range of settings including emergency departments, intensive care units and hospital wards
Since Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods are not absolutely superior to each other, we have used both of them simultaneously to improve the performance of the estimator
The Signal Purity Index (SPI) is calculated over time for each respiratory signal, and is used with an Extended Kalman Filter (EKF) to remove the noise from each respiratory signal

Summary

Introduction

Breathing Rate (BR) is a valuable physiological marker measured from patients in a wide range of settings including emergency departments, intensive care units and hospital wards. BR has been shown to be a sensitive indicator of patient deterioration. Elevated BRs may precede cardiac arrest or respiratory dysfunction [1]. BR can be used as a predictive index of in-hospital mortality [2]. Sensors are available for direct respiratory monitoring based on techniques such as spirometry, pneumography or plethysmography. These sensors can influence breathing patterns and can be obtrusive, and so their use is limited to specific clinical scenarios such as

Methods

Results

Conclusion