Blind source separation of inspiration and expiration in respiratory sEMG signals
Objective. Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort
in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac
activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory
electrical activity in single- or two-channel recordings. The main contribution of the presented
methodology is its applicability to the addressed muscles and the number of available channels.
Approach. We propose a two-step procedure consisting of a single-channel cardiac artifact removal
algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed
in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied
to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous
standard pneumatic measurements of the ventilated patient. Main results. The proposed estimation
scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system.
The results on the clinical datasets are validated based on expert annotations using invasive
pneumatic measurements. In the simulation, three measures evaluate the separation success: The
distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal
power ratio. We find an improvement across all SNRs, recruitment patterns, and channel
configurations. Moreover, our results in...
Source: Physiological Measurement - Category: Physiology Authors: Julia Sauer, Merle Streppel, Niklas M Carbon, Eike Petersen and Philipp Rostalski Source Type: research