Abstract
Electromyography (EMG) has long been used for the assessment of muscle function and activity and has recently been applied to the control of medical ventilation. For this application, the EMG signal is usually recorded invasively by means of electrodes on a nasogastric tube which is placed inside the esophagus in order to minimize noise and crosstalk from other muscles. Replacing these invasive measurements with an EMG signal obtained non-invasively on the body surface is difficult and requires techniques for signal separation in order to reconstruct the contributions of the individual respiratory muscles. In the case of muscles with small cross-sectional areas, or with muscles at large distances from the recording site, solutions to this problem have been proposed previously. The respiratory muscles, however, are large and distributed widely over the upper body volume. In this article, we describe an algorithm for convolutive blind source separation (BSS) that performs well even for large, distributed muscles such as the respiratory muscles, while using only a small number of electrodes. The algorithm is derived as a special case of the TRINICON general framework for BSS. To provide evidence that it shows potential for separating inspiratory, expiratory, and cardiac activities in practical applications, a joint numerical simulation of EMG and ECG activities was performed, and separation success was evaluated in a variety of noise settings. The results are promising.
Acknowledgments
We thank Philippe Jolliet and Lise Piquilloud at CHUV Lausanne for providing the clinical data sample used for the evaluation of the proposed algorithm.
Appendix A: Details of the EMG simulation model
The simulated diaphragm consisted of 300 MUs, with 20% being fast-twitch, fatigable MUs and 80% slow-twitch, fatigue resistant MUs. Both of the transversus abdominis muscles consisted of 100 MUs, with 60% being fast-twitch, fatigable MUs and 40% slow-twitch, fatigue resistant MUs. Fast-twitch MUs consisted of between 80 and 160 muscle fibers, while slow-twitch MUs consisted of between 40 and 80 muscle fibers (and had a lower conduction velocity and action potential amplitude).
MU locations were distributed uniformly in a prescribed muscle area. Each MU was then assigned a circular MU territory based on the number and type of muscle fibers belonging to the MU. This MU distribution strategy results in overlapping MU areas, which is a desirable property of models for MU geometry [15].
For MU rate coding and recruitment, a three-compartment piecewise linear model was employed that respects all of the empirical findings presented by De Luca and Erim [7]. Firing rates were assumed to lie in the range between 5 imp/s and 50 imp/s for the diaphragm, and between 5 imp/s and 70 imp/s for the transversus muscles. The mathematical details of the rate coding model may be the subject of a future publication; for the present investigation, however, the details of the rate coding model are irrelevant.
The EMG simulation was implemented in the R programming language [26].
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