Closed-loop acoustic stimulation during an afternoon nap to modulate subsequent encoding.

Sleep is able to contribute not only to memory consolidation, but also to post-sleep learning. The notion exists that either synaptic downscaling or another process during sleep increase post-sleep learning capacity. A correlation between augmentation of the sleep slow oscillation and hippocampal activation at encoding support the contribution of sleep to encoding of declarative memories. In the present study, the effect of closed-loop acoustic stimulation during an afternoon nap on post-sleep encoding of two verbal (word pairs, verbal learning and memory test) and non-verbal (figural pairs) tasks and on electroencephalogram during sleep and learning were investigated in young healthy adults (N = 16). Closed-loop acoustic stimulation enhanced slow oscillatory and spindle activity, but did not affect encoding at the group level. Subgroup analyses and comparisons with similar studies lead us to the tentative conclusion that further parameters such as time of day and subjects' cognitive ability influenced responses to closed-loop acoustic stimulation.

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.

Ferredoxin:NADPH oxidoreductase is recruited to thylakoids by binding to a polyproline type II helix in a pH-dependent manner.

Ferredoxin:NADPH oxidoreductase (FNR) is a key enzyme of photosynthetic electron transport required for generation of reduction equivalents. Recently, two proteins were found to be involved in membrane-anchoring of FNR by specific interaction via a conserved Ser/Pro-rich motif: Tic62 and Trol. Our crystallographic study reveals that the FNR-binding motif, which forms a polyproline type II helix, induces self-assembly of two FNR monomers into a back-to-back dimer. Because binding occurs opposite to the FNR active sites, its activity is not affected by the interaction. Surface plasmon resonance analyses disclose a high affinity of FNR to the binding motif, which is strongly increased under acidic conditions. The pH of the chloroplast stroma changes dependent on the light conditions from neutral to slightly acidic in complete darkness or to alkaline at saturating light conditions. Recruiting of FNR to the thylakoids could therefore represent a regulatory mechanism to adapt FNR availability/activity to photosynthetic electron flow.

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.

The path toward equal performance in medical machine learning.

To ensure equitable quality of care, differences in machine learning model performance between patient groups must be addressed. Here, we argue that two separate mechanisms can cause performance differences between groups. First, model performance may be worse than theoretically achievable in a given group. This can occur due to a combination of group underrepresentation, modeling choices, and the characteristics of the prediction task at hand. We examine scenarios in which underrepresentation leads to underperformance, scenarios in which it does not, and the differences between them. Second, the optimal achievable performance may also differ between groups due to differences in the intrinsic difficulty of the prediction task. We discuss several possible causes of such differences in task difficulty. In addition, challenges such as label biases and selection biases may confound both learning and performance evaluation. We highlight consequences for the path toward equal performance, and we emphasize that leveling up model performance may require gathering not only more data from underperforming groups but also better data. Throughout, we ground our discussion in real-world medical phenomena and case studies while also referencing relevant statistical theory.

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.