A Wearable Technology Revisited for Cardio-Respiratory Functional Exploration: Stroke Volume Estimation from Respiratory Inductive Plethysmography

A Wearable Technology Revisited for Cardio-Respiratory Functional Exploration: Stroke Volume Estimation from Respiratory Inductive Plethysmography

Julie Fontecave-Jallon (University Joseph Fourier-Grenoble 1, CNRS, TIMC-IMAG Laboratory CNRS UMR 5525, PRETA team, Grenoble, F-38041, France), Pierre-Yves Guméry (University Joseph Fourier-Grenoble 1, CNRS, TIMC-IMAG Laboratory CNRS UMR 5525, PRETA team, Grenoble, F-38041, France), Pascale Calabrese (University Joseph Fourier-Grenoble 1, CNRS, TIMC-IMAG Laboratory CNRS UMR 5525, PRETA team, Grenoble, F-38041, France), Raphaël Briot (University Joseph Fourier-Grenoble 1, CNRS, TIMC-IMAG Laboratory CNRS UMR 5525, PRETA team, Grenoble, F-38041, France) and Pierre Baconnier (University Joseph Fourier-Grenoble 1, CNRS, TIMC-IMAG Laboratory CNRS UMR 5525, PRETA team, Grenoble, F-38041, France)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/jehmc.2013010102
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Abstract

The objective of the present study is to extract new information from complex signals generated by Respiratory Inductive Plethysmography (RIP). This indirect cardio-respiratory (CR) measure is a well-known wearable solution. The authors applied time-scale analysis to estimate cardiac activity from thoracic volume variations, witnesses of CR interactions. Calibrated RIP signals gathered from 4 healthy volunteers in resting conditions are processed by Ensemble Empirical Mode Decomposition to extract cardiac volume signals and estimate stroke volumes. Averaged values of these stroke volumes (SVRIP) are compared with averaged values of stroke volumes determined simultaneously by electrical impedance cardiography (SVICG). There is a satisfactory correlation between SVRIP and SVICG (r=0.76, p<0.001) and the limits of agreement between the 2 types of measurements (±23%) satisfies the required criterion (±30%). The observed under-estimation (-58%) is argued. This validates the use of RIP for following stroke volume variations and suggests that one simple transducer can provide a quantitative exploration of both ventilatory and cardiac volumes.
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Introduction

Continuous monitoring of vital and behavioral signs is an emerging concept of healthcare. Although wearable technology is more and more implied in such a context (Lymberis & Dittmar, 2011), little attention is paid to its application for functional exploration. Nevertheless, smart shirt technology should provide a new way for non-invasive and yet performing tools in the field of daily medical practice. The classical challenge addressed in the studies dedicated to wearable solutions is the robustness of an indirect measurement devoted to a unique sign. As an example, Lanata et al. (2010) have compared the motion susceptibility of different wearable technologies for respiratory rate monitoring. Our challenge here is quite different. The indirect nature of the measurements generates a complexity in the signal due to multiple physiological interactions. Thus we aim to extract new information from these physiological interferences. This extraction will be conducted on a time-scale basis.

In this article, we propose an integrated physiological tool to study cardio-respiratory interactions, which are both of physiological and clinical interest (Bradley et al., 2010; Lalande & Johnson, 2010; Marcora et al., 2008). We investigated Respiratory Inductive Plethysmography (RIP) (Milledge & Stott, 1977) which is a wearable technology already tested for respiratory rate monitoring (Lanata et al., 2010; Grossman et al., 2010) and also used for ventilatory function assessment (Chadha et al., 1982; Tobin et al., 1983; Eberhard et al., 2001). We aim here to assess the estimation of cardiac parameters from the respiratory signal.

The nonlinear local technique, Empirical Mode Decomposition (EMD) has been proposed by Huang et al. (1998) for adaptively representing non-stationary signals as sums of zero-mean AM-FM components (Rilling et al., 2003). Empirical mode decomposition is a signal processing technique to extract all the oscillatory modes embedded in a signal without any requirement of stationarity or linearity of the data (Liang et al., 2005; Charleston-Villalobos et al., 2007). With the EMD technique, any complicated signal can be decomposed into a definite number of high-frequency and low-frequency components, which are called intrinsic mode functions (IMF).

In cardio-respiratory EMD applications reported in literature, the extracted modes are speculatively associated with specific physiological aspects of the phenomenon investigated. In Balocchi et al. (2004), it has been shown that EMD can be useful for estimating R-R interval variations due to respiration. The authors underlined that these variations are the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating a great amount of information content. In Bu et al. (2007), EMD was used to extract local temporal structures such as the heart beats superimposed on respiration signals in order to monitor respiration and cardiac frequencies during sleep using a flexible piezoelectric film sensor. These studies demonstrate the interest of the EMD in the cardio-respiratory context.

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