Stroke Volume Estimation from Respiratory Inductive Plethysmography: Double Empirical Decomposition

Enas Abdulhay,S Hariharasitaraman,Pierre-Yves Gumery,Gustavo Ramirez-Gonzalez,R Ilango,M Thilagaraj,N Arunkumar,Antonio J Peña

doi:10.1155/2022/9942367

Abstract

In this study, we have developed a “double-empirical mode decomposition algorithm” to estimate cardiac stroke volume from respiratory inductive plethysmography (RIP) signals. The algorithm consists of first an ensemble empirical mode decomposition (EEMD) to extract the cardiorespiratory components. Then, it is followed by an empirical mode decomposition (EMD) to extract only the cardiac components. This double approach permits (a) solving problems of mixing between cardiac and respiratory components (mode and scale mixing), (b) cardiogenic oscillations extraction in the respiratory inductive plethysmography signal, and (c) subsequent estimation of stroke volume. The algorithm is applied to simulated and real RIP signals. The simulated signals are generated by a cardiorespiratory model previously published by the authors. The real signals are measured via a developed inductive vest. In the real case, the values of estimated stroke volumes are compared to the values obtained by thoracocardiographic filter-based method. In the simulated case, the values are compared to the simulated cardiac activity. The results of comparison through Bland and Altman indicate an error lying in the range ±10%. In contrast to thoracocardiography, the proposed method consists of a promising tool for continuous noninvasive adaptive cardiac monitoring that does not need adjusting parameters or cut-off based on ECG. Also, in comparison to echocardiography and impedance-based methods, it does not necessitate the presence of an expert and is not too sensitive to current penetration.

Highlights

IMF1 and IMF2 are likely composed of noise. e cardiac signal seems to spread over the IMF3, IMF4, and IMF5
An implemented vest has been used according to the cardiorespiratory inductive plethysmography principle. en, a double decomposition algorithm has been applied to simulated and real measured cardiorespiratory signal in order to extract cardiac activity. e simulated signals are generated by a previous model built by the authors
In the CR model, a first step has been achieved to the simulation of the physiological shapes of mechanical cardiac activity as well as respiratory waves. e double decomposition includes two steps: first step is an ensemble empirical mode decomposition. e second step is an empirical mode decomposition of selected components extracted from step 1

Summary

Introduction

Erefore, noninvasive procedures have been implemented, e.g., transthoracic electrical bioimpedance and echocardiography. Those techniques are not commonly valid for routine use for the reason that their efficacy in clinical situations and in continuous monitoring is still under examination [1]. A noninvasive technique, named thoracocardiography (TCG), has been introduced in [2] It is centered on the concept of inductive plethysmography (IP). TCG targets the noninvasive recording of left ventricular stroke volume by ECG-triggered ensemble averaging and bandpass filtering [0.7 ∗ (heart rate) Hz–10 Hz] [3] of the resulted signal with the purpose of elimination of low frequency, associated with breathing and body artifacts, as well as of high-frequency noise. The cut-off values of the used filter are highly dependent on heart rate, TCG cannot be suitable in strong nonstationary conditions

Methods

Results

Conclusion