Blind source separation of inspiration and expiration in respiratory sEMG signals

Abstract

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 indicate that the initialization strategy replaces the manual matching of sources after the BSS. Significance: The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.

Automated characterization of patient–ventilator interaction using surface electromyography

“…In future works, the detection performance might be improved by automated artifact and crosstalk rejection [ 26 ]. Furthermore, the complementary properties of the two detection algorithms suggest the potential for combining the strengths of both approaches, e.g., by averaging over or switching between them.…”

Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation

“…Some of these methods involve the analysis of temporal envelope [13], autocorrelation [14], machine learning algorithms such as random forest [15] and k-nearest neighbour [16], entropy-based approaches [17], harmonic product spectrum analysis [18], convolutional neural networks [19], long-short term memory networks [20], fundamental frequency extraction employing adaptive thresholding [18], and the Hilbert transform [13], among others. Recently, non-negative matrix factorization (NMF) has been applied in the field of RR estimation, although these approaches have not used audio signals [21,22]. The versatility of NMF and its potential to extract meaningful information from complex data sources make it an intriguing avenue for further exploration.…”