Key result
A double-empirical mode decomposition algorithm estimated cardiac stroke volume from respiratory inductive plethysmography signals with an error range of ±10% compared to reference methods.
Why the study?
Existing methods for continuous noninvasive cardiac monitoring face challenges such as mode mixing between cardiac and respiratory components in respiratory inductive plethysmography signals and reliance on ECG cut-offs or expert operators.
Effect estimate: Error range ±10%
A novel double-empirical mode decomposition algorithm can estimate stroke volume from respiratory inductive plethysmography signals with ±10% error, offering a potential tool for continuous noninvasive cardiac monitoring.
Not ready for clinical use; leaves open prospective validation of respiratory-based stroke volume estimation.
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.
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Abdulhay et al. (2022) studied this question. Double-empirical mode decomposition algorithm vs. Thoracocardiographic filter-based method and simulated cardiac activity was evaluated on Stroke volume estimation error (Error range ±10%). A double-empirical mode decomposition algorithm estimated cardiac stroke volume from respiratory inductive plethysmography signals with an error range of ±10% compared to reference methods.
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