Electromyography (EMG) has long been used for the assessment of muscle function and activity and recently found its way into 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 by 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 area, or with muscles at large distance 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 the potential of separating between inspiratory, expiratory and cardiac activity in practical applications, a joint numerical simulation of EMG and ECG activity is performed, and separation success is evaluated in a variety of noise settings. The results are promising.
The article will be published in a special issue on "Smart Life Support Systems" of the journal "Biomedical Engineering / Biomedizinische Technik".
