Surface EMG-based quantification of inspiratory effort: a quantitative comparison with Pes.

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 [Formula: see text], derived from esophageal pressure ([Formula: see text]), 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 [Formula: see text]-time product ([Formula: see text]), the transdiaphragmatic pressure-time product ([Formula: see text]) 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 [Formula: see text] was assessed by determining a neuromechanical conversion factor [Formula: see text] 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 [Formula: see text] ([Formula: see text] and [Formula: see text] for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with [Formula: see text] ([Formula: see text]). The neuromechanical conversion factor obtained by fitting ETP to [Formula: see text] varied widely between patients ([Formula: see text]) and was highly correlated with the scalar determined during occlusions ([Formula: see text], [Formula: see text]). The occlusion-based method for deriving [Formula: see text] from ETP showed a breath-wise deviation to [Formula: see text] of [Formula: see text] 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.

Determination of respiratory system compliance during pressure support ventilation by small variations of pressure support.

In mechanically ventilated patients, measurement of respiratory system compliance (Crs) is of high clinical interest. Spontaneous breathing activity during pressure support ventilation (PSV) can impede the correct assessment of Crs and also alter the true Crs by inducing lung recruitment. We describe a method for determination of Crs during PSV and assess its accuracy in a study on 20 mechanically ventilated patients. To assess Crs during pressure support ventilation (Crs,PSV), we performed repeated changes in pressure support level by ± 2 cmH2O. Crs,PSV was calculated from the volume change induced by these changes in pressure support level, taking into account the inspiration time and the expiratory time constant. As reference methods, we used Crs, measured during volume controlled ventilation (Crs,VCV). In a post-hoc analysis, we assessed Crs during the last 20% of the volume-controlled inflation (Crs,VCV20). Values were compared by linear regression and Bland-Altman methods comparison. Comparing Crs,PSV to the reference value Crs,VCV, we found a coefficient of determination (r2) of 0.90, but a relatively high bias of - 7 ml/cm H2O (95% limits of agreement - 16.7 to + 2.7 ml/cmH2O). Comparison with Crs,VCV20 resulted in a negligible bias (- 1.3 ml/cmH2O, 95% limits of agreement - 13.9 to + 11.3) and r2 of 0.81. We conclude that the novel method provides an estimate of end-inspiratory Crs during PSV. Despite its limited accuracy, it might be useful for non-invasive monitoring of Crs in patients undergoing pressure support ventilation.

Automated characterization of patient-ventilator interaction using surface electromyography.

BACKGROUND: Characterizing patient-ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient. METHODS: In this study, we recorded surface electromyography ([Formula: see text]) signals from the diaphragm and intercostal muscles and esophageal pressure ([Formula: see text]) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in [Formula: see text], blinded toward each other, the [Formula: see text] signals, and the algorithmic results. We also classified patient-ventilator interaction and calculated asynchrony indices with manually detected inspirations in [Formula: see text] as a reference for automated asynchrony classification and asynchrony index calculation. RESULTS: Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in [Formula: see text] demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in [Formula: see text]. The average delay of automatically detected inspiratory onset to the [Formula: see text] reference was [Formula: see text]79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of [Formula: see text] to the [Formula: see text]-based reference. CONCLUSIONS: Our study demonstrates the feasibility of automating the quantification of patient-ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient-ventilator interaction.

Model-Based Estimation of Inspiratory Effort Using Surface EMG.

OBJECTIVE: The quantification of inspiratory patient effort in assisted mechanical ventilation is essential for the adjustment of ventilatory assistance and for assessing patient-ventilator interaction. The inspiratory effort is usually measured via the respiratory muscle pressure (P mus) derived from esophageal pressure (P es) measurements. As yet, no reliable non-invasive and unobtrusive alternatives exist to continuously quantify P mus. METHODS: We propose a model-based approach to estimate P mus non-invasively during assisted ventilation using surface electromyographic (sEMG) measurements. The method combines the sEMG and ventilator signals to determine the lung elastance and resistance as well as the neuromechanical coupling of the respiratory muscles via a novel regression technique. Using the equation of motion, an estimate for P mus can then be calculated directly from the lung mechanical parameters and the pneumatic ventilator signals. RESULTS: The method was applied to data recorded from a total of 43 ventilated patients and validated against P es-derived P mus. Patient effort was quantified via the P mus pressure-time-product (PTP). The sEMG-derived PTP estimated using the proposed method was highly correlated to P es-derived PTP ([Formula: see text]), and the breath-wise deviation between the two quantities was [Formula: see text]. CONCLUSION: The estimated, sEMG-derived P mus is closely related to the P es-based reference and allows to reliably quantify inspiratory effort. SIGNIFICANCE: The proposed technique provides a valuable tool for physicians to assess patients undergoing assisted mechanical ventilation and, thus, may support clinical decision making.

Anatomy-guided domain adaptation for 3D in-bed human pose estimation.

3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalization under domain shifts along with their need for sufficient labeled training data. As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method comprises two complementary adaptation strategies based on prior knowledge about human anatomy. First, we guide the learning process in the target domain by constraining predictions to the space of anatomically plausible poses. To this end, we embed the prior knowledge into an anatomical loss function that penalizes asymmetric limb lengths, implausible bone lengths, and implausible joint angles. Second, we propose to filter pseudo labels for self-training according to their anatomical plausibility and incorporate the concept into the Mean Teacher paradigm. We unify both strategies in a point cloud-based framework applicable to unsupervised and source-free domain adaptation. Evaluation is performed for in-bed pose estimation under two adaptation scenarios, using the public SLP dataset and a newly created dataset. Our method consistently outperforms various state-of-the-art domain adaptation methods, surpasses the baseline model by 31%/66%, and reduces the domain gap by 65%/82%. Source code is available at https://github.com/multimodallearning/da-3dhpe-anatomy.

Muscle Artifact Removal in Single-Channel Electrocardiograms using Temporal Convolutional Networks.

The electrocardiogram (ECG) is a vital diagnostic tool used in many health applications. In practice, interference by muscle artifacts is very common and may significantly complicate interpretation of the ECG waveform. In this work we investigate the removal of muscle artifacts in single-channel ECG signals using neural network models. To this end, we compare two neural network architectures which were previously used for ECG denoising and propose a novel third method based on the ConvTasNet. The neural networks are trained on simulated data using artificial mixtures of single-channel ECG (lead II) and surface EMG signals taken from publicly available datasets. ECG data were sampled from the MIT-BIH Arrhythmia and the PTB-XL database. The former provides recordings of ambulatory ECGs while the latter contains a large variety of cardiac pathologies recorded in a clinical setting. The muscle artifacts were sampled from the MIT-BIH Noise Stress Test database and the TaiChi database. In the past, most denoising methods were only tested on the two smaller MIT-BIH datasets. In this work, we report performances on larger datasets and thus provide stronger evidence for a clinical use-case. We also report out-of distribution performance of the three methods by switching the ECG dataset between training and test. The herein investigated variant of the ConvTasNet substantially reduces interference by muscle artifacts, outperforms state-of-the-art methods and thus, may support clinical decision making.

Comfortable Body Surface Potential Mapping by Means of a Dry Electrode Belt.

Body Surface Potential Mapping is the spatial high-resolution acquisition of cardiac electrical activity from the thorax surface. The method is used to record more comprehensive cardiac information than conventional ECG measurement approaches. Although Body Surface Potential Mapping is well-known and is technically feasible, it is rarely used in clinical environments. One reason for this is the cumbersome procedure of a measurement. The placement of many adhesive gel electrodes and the contacting with many cables are particularly problematic. These limit both patients and medical staff. Therefore, the goal of this work is to technically simplify Body Surface Potential Mapping so that it would be applicable under clinical conditions. For this purpose, we present a new measurement approach in which only a narrow elastic belt is placed around the thorax to measure the electrical activity of the heart. This belt is equipped with an array of reusable gold-plated dry electrodes. With these dry electrodes, the differential voltages are measured in the horizontal and vertical directions. Afterwards, an approximation of the geometrical potential distribution on the thorax is obtained from these measurements. The results are then visualized as videos or image series or used for further analysis. A subject measurement demonstrates the applicability of this novel approach. It is shown that the obtained Body Surface Potential Maps are very similar to those found in the literature, despite a reduced spatial measurement range. This approach is not only applicable for clinical applications but also suitable for monitoring during physiological training.

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 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.

A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation.

Neuromuscular physiology is a vibrant research field that has recently seen exciting advances. Previous publications have focused on thorough analyses of particular aspects of neuromuscular physiology, yet an integration of the various novel findings into a single, comprehensive model is missing. In this article, we provide a unified description of a comprehensive mathematical model of surface electromyographic (EMG) measurements and the corresponding force signal in skeletal muscles, both consolidating and extending the results of previous studies regarding various components of the neuromuscular system. The model comprises motor unit (MU) pool organization, recruitment and rate coding, intracellular action potential generation and the resulting EMG measurements, as well as the generated muscular force during voluntary isometric contractions. Mathematically, it consists of a large number of linear PDEs, ODEs, and various stochastic nonlinear relationships, some of which are solved analytically, others numerically. A parameterization of the electrical and mechanical components of the model is proposed that ensures a physiologically meaningful EMG-force relation in the simulated signals, in particular taking the continuous, size-dependent distribution of MU parameters into account. Moreover, a novel nonlinear transformation of the common drive model input is proposed, which ensures that the model force output equals the desired target force. On a physiological level, this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle, while from a control theoretic point of view, this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system. Finally, an alternative analytical formulation of the EMG model is proposed, which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink. A consistent description of a complete physiological model as presented here, including thorough justification of model component choices, will facilitate the use of these advanced models in future research. Results of a numerical simulation highlight the model's capability to reproduce many physiological effects observed in experimental measurements, and to produce realistic synthetic data that are useful for the validation of signal processing algorithms.

Automatic Estimation of Respiratory Effort using Esophageal Pressure.

Esophageal pressure is currently seen as the gold standard to quantify the respiratory effort during assisted spontaneous ventilation. Yet, the assessment of waveforms at the bedside is often complicated due to heavy interference by cardiac artifacts and due to the unknown dependency on the lung volume. We propose an algorithm that automatically removes artifacts and gives an estimate for the respiratory effort of a patient. The estimator is based on fitting a respiratory system model to the Campbell diagram and, thus, also gives insight into important patient parameters like the chest wall elastance. The feasibility of our approach is demonstrated using clinical datasets of patients on pressure support ventilation. The algorithm facilitates the interpretation of ventilatory waveforms and may support the overall assessment of patients.

Convolutive blind source separation of surface EMG measurements of the respiratory muscles.

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.

A template subtraction method for the removal of cardiogenic oscillations on esophageal pressure signals.

Esophageal pressure (Pes) is usually measured in patients receiving mechanical ventilation and is used for the assessment of lung mechanics. However, its interpretation is complicated by the presence of cardiogenic oscillations (CGO). In this article we present a novel method for the reduction of CGO based on the identification of pressure templates. Similar approaches are known for the removal of electrocardiographic (ECG) artifacts from the electromyogram (EMG). The proposed method is tested on clinical recordings of patients under assisted spontaneous ventilation. Besides the improvement of the respiratory signals, the identified CGO templates can be used diagnostically when viewed in relation to corresponding ECG data. This approach is illustrated on a few sample datasets.

Convolutive blind source separation on surface EMG signals for respiratory diagnostics and medical ventilation control.

The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system.