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Sauer J, Streppel M, Carbon NM, Petersen E, Rostalski P. Blind source separation of inspiration and expiration in respiratory sEMG signals. Physiol Meas 2022; 43. [PMID: 35709716 DOI: 10.1088/1361-6579/ac799c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/16/2022] [Indexed: 11/11/2022]
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.
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Affiliation(s)
- Julia Sauer
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Lübeck, 23562, GERMANY
| | - Merle Streppel
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Lübeck, 23562, GERMANY
| | - Niklas Martin Carbon
- Department of Anesthesiology and Intensive Care Medicine, Charite Universitatsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Berlin, Berlin, 10117, GERMANY
| | - Eike Petersen
- DTU Compute, Technical University of Denmark, Richard Petersens Plads, Lyngby, 2800, DENMARK
| | - Philipp Rostalski
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Ratzeburger Allee 160, Lübeck, Schleswig-Holstein, 23562, GERMANY
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Rodrigues A, Janssens L, Langer D, Matsumura U, Rozenberg D, Brochard L, Reid WD. Semi-automated Detection of the Timing of Respiratory Muscle Activity: Validation and First Application. Front Physiol 2022; 12:794598. [PMID: 35046839 PMCID: PMC8762204 DOI: 10.3389/fphys.2021.794598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/26/2021] [Indexed: 11/24/2022] Open
Abstract
Background: Respiratory muscle electromyography (EMG) can identify whether a muscle is activated, its activation amplitude, and timing. Most studies have focused on the activation amplitude, while differences in timing and duration of activity have been less investigated. Detection of the timing of respiratory muscle activity is typically based on the visual inspection of the EMG signal. This method is time-consuming and prone to subjective interpretation. Aims: Our main objective was to develop and validate a method to assess the respective timing of different respiratory muscle activity in an objective and semi-automated manner. Method: Seven healthy adults performed an inspiratory threshold loading (ITL) test at 50% of their maximum inspiratory pressure until task failure. Surface EMG recordings of the costal diaphragm/intercostals, scalene, parasternal intercostals, and sternocleidomastoid were obtained during ITL. We developed a semi-automated algorithm to detect the onset (EMG, onset) and offset (EMG, offset) of each muscle’s EMG activity breath-by-breath with millisecond accuracy and compared its performance with manual evaluations from two independent assessors. For each muscle, the Intraclass Coefficient correlation (ICC) of the EMG, onset detection was determined between the two assessors and between the algorithm and each assessor. Additionally, we explored muscle differences in the EMG, onset, and EMG, offset timing, and duration of activity throughout the ITL. Results: More than 2000 EMG, onset s were analyzed for algorithm validation. ICCs ranged from 0.75–0.90 between assessor 1 and 2, 0.68–0.96 between assessor 1 and the algorithm, and 0.75–0.91 between assessor 2 and the algorithm (p < 0.01 for all). The lowest ICC was shown for the diaphragm/intercostal and the highest for the parasternal intercostal (0.68 and 0.96, respectively). During ITL, diaphragm/intercostal EMG, onset occurred later during the inspiratory cycle and its activity duration was shorter than the scalene, parasternal intercostal, and sternocleidomastoid (p < 0.01). EMG, offset occurred synchronously across all muscles (p ≥ 0.98). EMG, onset, and EMG, offset timing, and activity duration was consistent throughout the ITL for all muscles (p > 0.63). Conclusion: We developed an algorithm to detect EMG, onset of several respiratory muscles with millisecond accuracy that is time-efficient and validated against manual measures. Compared to the inherent bias of manual measures, the algorithm enhances objectivity and provides a strong standard for determining the respiratory muscle EMG, onset.
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Affiliation(s)
- Antenor Rodrigues
- Department of Critical Care, St. Michael's Hospital, Toronto, ON, Canada
| | - Luc Janssens
- Department of Electrical Engineering, Faculty of Engineering Technology, Katholieke Universiteit Leuven, Leuven, Belgium.,Department of Rehabilitation Sciences, Faculty of Movement and Rehabilitation Sciences, Research Group for Rehabilitation in Internal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Daniel Langer
- Department of Rehabilitation Sciences, Faculty of Movement and Rehabilitation Sciences, Research Group for Rehabilitation in Internal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium.,Respiratory Rehabilitation and Respiratory Division, University Hospital Leuven, Leuven, Belgium
| | - Umi Matsumura
- Department of Physiotherapy, Nagasaki University, Nagasaki, Japan
| | - Dmitry Rozenberg
- Division of Respirology, Temerty Faculty of Medicine, University of Toronto, University Health Network, Toronto, ON, Canada.,Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Laurent Brochard
- Department of Critical Care, St. Michael's Hospital, Toronto, ON, Canada.,Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada.,Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - W Darlene Reid
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada.,Department of Physical Therapy, University of Toronto, Toronto, ON, Canada.,KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
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Graßhoff J, Petersen E, Farquharson F, Kustermann M, Kabitz HJ, Rostalski P, Walterspacher S. Surface EMG-based quantification of inspiratory effort: a quantitative comparison with P es. Crit Care 2021; 25:441. [PMID: 34930396 PMCID: PMC8686581 DOI: 10.1186/s13054-021-03833-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/19/2021] [Indexed: 11/26/2022] Open
Abstract
Background Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient–ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure \documentclass[12pt]{minimal}
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\begin{document}$$\textit{P}_{\mathrm{mus}}$$\end{document}Pmus, derived from esophageal pressure (\documentclass[12pt]{minimal}
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\begin{document}$$\textit{P}_{\mathrm{es}}$$\end{document}Pes), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity. Methods A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the \documentclass[12pt]{minimal}
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\begin{document}$$\textit{P}_{\mathrm{mus}}$$\end{document}Pmus-time product (\documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus), the transdiaphragmatic pressure-time product (\documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{di}}$$\end{document}PTPdi) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus was assessed by determining a neuromechanical conversion factor \documentclass[12pt]{minimal}
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\begin{document}$$\textit{K}_{\mathrm{EMG}}$$\end{document}KEMG between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements. Results In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus (\documentclass[12pt]{minimal}
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\begin{document}$$\textit{r}={0.79\pm 0.25}$$\end{document}r=0.79±0.25 and \documentclass[12pt]{minimal}
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\begin{document}$$\textit{r}={0.84\pm 0.16}$$\end{document}r=0.84±0.16 for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus (\documentclass[12pt]{minimal}
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\begin{document}$$\textit{r}={0.87\pm 0.09}$$\end{document}r=0.87±0.09). The neuromechanical conversion factor obtained by fitting ETP to \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus varied widely between patients (\documentclass[12pt]{minimal}
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\begin{document}$$\textit{K}_{\mathrm{EMG}}= {4.32\pm 3.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}/\upmu \hbox {V}}$$\end{document}KEMG=4.32±3.73cm2O/μV) and was highly correlated with the scalar determined during occlusions (\documentclass[12pt]{minimal}
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\begin{document}$$\textit{r}={0.95}$$\end{document}r=0.95, \documentclass[12pt]{minimal}
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\begin{document}$$\textit{p}<{.001}$$\end{document}p<.001). The occlusion-based method for deriving \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus from ETP showed a breath-wise deviation to \documentclass[12pt]{minimal}
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\begin{document}$${\mathrm{PTP}}_{\mathrm{mus}}$$\end{document}PTPmus of \documentclass[12pt]{minimal}
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\begin{document}$${0.43\pm 1.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}\,\hbox {s}}$$\end{document}0.43±1.73cm2Os across all datasets. Conclusion These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-021-03833-w.
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Affiliation(s)
- Jan Graßhoff
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Moislinger Allee 53-55, 23558, Lübeck, Germany. .,Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Mönkhofer Weg 239 a, 23562, Lübeck, Germany.
| | - Eike Petersen
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Moislinger Allee 53-55, 23558, Lübeck, Germany
| | | | - Max Kustermann
- Medical Clinic II, Klinikum Konstanz, Mainaustraße 35, 78464, Konstanz, Germany
| | - Hans-Joachim Kabitz
- Medical Clinic II, Klinikum Konstanz, Mainaustraße 35, 78464, Konstanz, Germany
| | - Philipp Rostalski
- Institute for Electrical Engineering in Medicine, Universität zu Lübeck, Moislinger Allee 53-55, 23558, Lübeck, Germany.,Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Mönkhofer Weg 239 a, 23562, Lübeck, Germany
| | - Stephan Walterspacher
- Medical Clinic II, Klinikum Konstanz, Mainaustraße 35, 78464, Konstanz, Germany.,Faculty of Health/School of Medicine, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, 58455, Witten, Germany
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