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.

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.

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.

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.