An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. METHODS: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. RESULTS: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01). CONCLUSIONS: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography.

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Reliability of whole mount radical prostatectomy histopathology as the ground truth for artificial intelligence assisted prostate imaging.

The development of artificial intelligence-based imaging techniques for prostate cancer (PCa) detection and diagnosis requires a reliable ground truth, which is generally based on histopathology from radical prostatectomy specimens. This study proposes a comprehensive protocol for the annotation of prostatectomy pathology slides. To evaluate the reliability of the protocol, interobserver variability was assessed between five pathologists, who annotated ten radical prostatectomy specimens consisting of 74 whole mount pathology slides. Interobserver variability was assessed for both the localization and grading of PCa. The results indicate excellent overall agreement on the localization of PCa (Gleason pattern ≥ 3) and clinically significant PCa (Gleason pattern ≥ 4), with Dice similarity coefficients (DSC) of 0.91 and 0.88, respectively. On a per-slide level, agreement for primary and secondary Gleason pattern was almost perfect and substantial, with Fleiss Kappa of .819 (95% CI .659-.980) and .726 (95% CI .573-.878), respectively. Agreement on International Society of Urological Pathology Grade Group was evaluated for the index lesions and showed agreement in 70% of cases, with a mean DSC of 0.92 for all index lesions. These findings show that a standardized protocol for prostatectomy pathology annotation provides reliable data on PCa localization and grading, with relatively high levels of interobserver agreement. More complicated tissue characterization, such as the presence of cribriform growth and intraductal carcinoma, remains a source of interobserver variability and should be treated with care when used in ground truth datasets.

On the distinct differences in autonomic regulation between pregnant and non-pregnant women - a heart rate variability analysis.

Objective. Appropriate adaptation of the maternal autonomic nervous system to progressing gestation is essential to a healthy pregnancy. This is partly evidenced by the association between pregnancy complications and autonomic dysfunction. Therefore, assessing maternal heart rate variability (HRV)-a proxy measure for autonomic activity-may offer insights into maternal health, potentially enabling the early detection of complications. However, identifying abnormal maternal HRV requires a thorough understanding of normal maternal HRV. While HRV in women of childbearing age has been extensively investigated, less is known concerning HRV during pregnancy. Subsequently, we investigate the differences in HRV between healthy pregnant women and their non-pregnant counterparts.Approach. We use a comprehensive suite of HRV features (assessing sympathetic and parasympathetic activity, heart rate (HR) complexity, HR fragmentation, and autonomic responsiveness) to quantify HRV in large groups of healthy pregnant (n= 258) and non-pregnant women (n= 252). We compare the statistical significance and effect size of the potential differences between the groups.Main results. We find significantly increased sympathetic and decreased parasympathetic activity during healthy pregnancy, along with significantly attenuated autonomic responsiveness, which we hypothesize serves as a protective mechanism against sympathetic overactivity. HRV differences between these groups typically had a large effect size (Cohen'sd> 0.8), with the largest effect accompanying the significantly reduced HR complexity and altered sympathovagal balance observed in pregnancy (Cohen'sd> 1.2).Significance. Healthy pregnant women are autonomically distinct from their non-pregnant counterparts. Subsequently, assumptions based on HRV research in non-pregnant women cannot be readily translated to pregnant women.

Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation.

There is a clinical need for monitoring inspiratory effort to prevent lung- and diaphragm injury in patients who receive supportive mechanical ventilation in an Intensive Care Unit. Different pressure-based techniques are available to estimate this inspiratory effort at the bedside, but the accuracy of their effort estimation is uncertain since they are all based on a simplified linear model of the respiratory system, which omits gas compressibility of air, and the viscoelasticity and nonlinearities of the respiratory system. The aim of this in-silico study was to provide an overview of the pressure-based estimation techniques and to evaluate their accuracy using a more sophisticated model of the respiratory system and ventilator. The influence of the following parameters on the accuracy of the pressure-based estimation techniques was evaluated using the in-silico model: 1) the patient's respiratory mechanics 2) PEEP and the inspiratory pressure of the ventilator 3) gas compressibility of air 4) viscoelasticity of the respiratory system 5) the strength of the inspiratory effort. The best-performing technique in terms of accuracy was the whole breath occlusion. The average error and maximum error were the lowest for all patient archetypes. We found that the error was related to the expansion of gas in the breathing set and lungs and respiratory compliance. However, concerns exist that other factors not included in the model, such as a changed muscle-force relation during an occlusion, might influence the true accuracy. The estimation techniques based on the esophageal pressure showed an error related to the viscoelastic element in the model which leads to a higher error than the occlusion. The error of the esophageal pressure-based techniques is therefore highly dependent on the pathology of the patient and the settings of the ventilator and might change over time while a patient recovers or becomes more ill.

Characterizing the effect of demographics, cardiorespiratory factors, and inter-subject variation on maternal heart rate variability in pregnancy with statistical modeling: a retrospective observational analysis.

Pregnancy complications are associated with insufficient adaptation of the maternal autonomic nervous system to the physiological demands of pregnancy. Consequently, assessing maternal heart rate variability (mHRV)-which reflects autonomic regulation-is a promising tool for detecting early deterioration in maternal health. However, before mHRV can be used to screen for complications, an understanding of the factors influencing mHRV during healthy pregnancy is needed. In this retrospective observational study, we develop regression models to unravel the effects of maternal demographics (age, body mass index (BMI), gestational age (GA), and parity), cardiorespiratory factors (heart rate and breathing rate), and inter-subject variation on mHRV. We develop these models using two datasets which are comprised of, respectively, single measurements in 290 healthy pregnant women and repeated measurements (median = 8) in 29 women with healthy pregnancies. Our most consequential finding is that between one-third and two-thirds of the variation in mHRV can be attributed to inter-subject variability. Additionally, median heart rate dominantly affects mHRV (p < 0.001), while BMI and parity have no effect. Moreover, we found that median breathing rate, age, and GA all impact mHRV (p < 0.05). These results suggest that personalized, long-term monitoring would be necessary for using mHRV for obstetric screening.

Maternal autonomic responsiveness is attenuated in healthy pregnancy: a phase rectified signal averaging analysis.

Autonomic regulation is essential in enabling a healthy pregnancy. In fact, several pregnancy complications are associated with autonomic dysfunction. Better understanding of the maternal autonomic state during healthy pregnancy may aid in the early detection of such complications. One aspect of autonomic regulation is autonomic responsiveness, which can by assessed by phase rectified signal averaging (PRSA). While other areas of research have found blunted physiological responses in pregnancy, this paper presents the first investigation of maternal autonomic responsiveness as assessed by PRSA. We find significantly reduced rates of responses, as well as an attenuated capacity for heart rate acceleration when comparing pregnant women to non-pregnant controls. We hypothesize that this attenuated autonomic control may serve to protect the mother against her imbalanced autonomic state, as increased sympathetic and decreased parasympathetic modulation accompany healthy pregnancies. Clinical Relevance- Maternal autonomic responsiveness is attenuated in pregnancy in comparison to non-pregnant women. Understanding maternal autonomic state not only improves our knowledge of gestational physiology but also forms the basis for the early detection of pregnancy complications associated with maternal autonomic dysfunction.

A model-based approach to generating annotated pressure support waveforms.

Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. In this work, we propose a method for generating a large, realistic and labeled, synthetic dataset for training and validating machine learning algorithms to detect a wide variety of asynchrony types. We take a model-based approach in which we adapt a non-linear lung-airway model for use in a diverse patient group and add a first-order ventilator model to generate labeled pressure, flow, and volume waveforms of pressure support ventilation. The model was able to reproduce basic measured lung mechanics parameters. Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher's exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.

A Model-based Approach to Generating Annotated Pressure Support Waveforms.

During pressure support ventilation, every breath is triggered by the patient. Mismatches between the patient and the ventilator are called asynchronies. It has been reported that large numbers of asynchronies may be harmful and may lead to increased mortality. Automatic asynchrony detection and classification, with subsequent feedback to clinicians, will improve lung ventilation and, possibly, patient outcome. Machine learning techniques have been used to detect asynchronies. However, large, diverse and high-quality training and verification data sets are needed. In this work, we propose a model for generating a large, realistic, labeled, synthetic dataset for training and testing machine learning algorithms to detect a wide variety of asynchrony types. Next to a morphological evaluation of the obtained waveforms, validation of the proposed model includes a test with a machine learning algorithm trained on clinical data.

A machine learning method for automatic detection and classification of patient-ventilator asynchrony.

Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in the intensive care unit. In current clinical practice PVA is still detected using visual inspection of the air pressure, flow, and volume curves, which is time-consuming and sensitive to subjective interpretation. Correct detection of the patient respiratory efforts is needed to properly asses the type of asynchrony. Therefore, we propose a method for automatic detection of the patient respiratory efforts using a one-dimensional convolution neural network. The proposed method was able to detect patient efforts with a sensitivity and precision of 98.6% and 97.3% for the inspiratory efforts, and 97.7% and 97.2% for the expiratory efforts. Besides allowing detection of PVA, combining the estimated timestamps of patient's inspiratory and expiratory efforts with the timings of the mechanical ventilator further allows for classification of the asynchrony type. In the future, the proposed method could support clinical decision making by informing clinicians on the quality of ventilation and providing actionable feedback for properly adjusting the ventilator settings.

Method for measurement of arterial compliance by fusion of oscillometry and pulse wave velocity.

Up until now estimation of arterial compliance has been performed either by analysis of arterial pressure changes with respect to volume changes or by inference based on pulse wave velocity (PWV). In this study we demonstrate the possibility of an approach to assess arterial compliance by fusing the two information sources namely the pressure/volume relationship obtained from oscillography and PWV data. The goal is to assess arterial properties easily and robustly, enhancing current hemodynamic monitoring. The approach requires as input signals: an electrocardiogram (ECG), a photo- plethysmogram (PPG) and the arterial oscillation as measured during non-invasive blood pressure measurements based on oscillometry with a cuff. These signals are fused by an algorithm using Bayesian principles underpinned by a physiological model. In our simulations, we demonstrate the feasibility to infer arterial compliance by our proposed strategy. A very first measurement on a healthy volunteer supports our findings from the simulation.Clinical Relevance- Arterial compliance/stiffness is recognized as a key hemodynamic parameter, which is not easily accessible and not a standard parameter currently. The presented method and obtained results are encouraging for future research in this area.

Relevance of spectral peaks in electromyographic recordings during force-modulated vibration exercise.

Vibration exercise (VE) has been suggested for effective muscle training and conditioning. Surface electromyogram (EMG) is employed as a powerful tool for the analysis of VE. However, sharp peaks are observed in the EMG spectrum. The interpretation of these peaks is controversial, complicating the extraction of EMG parameters for VE analysis. The present study is therefore aiming at quantifying the relevance of these spectral peaks in EMG recording during VE. To this end, surface EMG was recorded on the biceps brachii during VE at different amplitudes and frequencies. The power percentage contained in the two narrow bands (±0.5 Hz) around the vibration frequency and its first harmonic (PPv) was calculated. The root mean square (RMS) value of the EMG was calculated with and without including the spectral peaks and then compared. The results show an average PPv value of 20.7 ± 7.9 % and a relative RMS difference (∆RMS) of 12.2 ± 3.8 %. In PPv and addition, RMS∆ seem to be influenced by vibration amplitude and frequency. Our results provide useful information for the analysis of VE and contribute to better understanding of the EMG spectral peaks.

Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure.

Monitoring the progression of uterine activity provides important prognostic information during pregnancy and delivery. Currently, uterine activity monitoring relies on direct or indirect mechanical measurements of intrauterine pressure (IUP), which are unsuitable for continuous long-term observation. The electrohysterogram (EHG) provides a non-invasive alternative to the existing methods and is suitable for long-term ambulatory use. Several published state-of-the-art methods for EHG-based IUP estimation are here discussed, analyzed, optimized, and compared. By means of parameter space exploration, key parameters of the methods are evaluated for their relevance and optimal values. We have optimized all methods towards higher IUP estimation accuracy and lower computational complexity. Their accuracy was compared with the gold standard accuracy of internally measured IUP. Their computational complexity was compared based on the required number of multiplications per second (MPS). Significant reductions in computational complexity have been obtained for all published algorithms, while improving IUP estimation accuracy. A correlation coefficient of 0.72 can be obtained using fewer than 120 MPS. We conclude that long-term ambulatory monitoring of uterine activity is possible using EHG-based methods. Furthermore, the choice of a base method for IUP estimation is less important than the correct selection of electrode positions, filter parameters, and postprocessing methods. The presented review of state-of-the-art methods and applied optimizations show that long-term ambulatory IUP monitoring is feasible using EHG measurements.

Improvement instead of stability in embryo quality between day 3-5: A possible extra predictor for blastocyst selection.

OBJECTIVE: The aim of this study was to evaluate the predictive value of the dynamic morphological development process between cleavage-stage and blastocyst-stage embryos. STUDY DESIGN: A retrospective study was executed between 2015 and 2017 at Ghent University Hospital. A total of 996 first fresh IVF/ICSI cycles resulting in a single embryo transfer on day 5 were included. Embryos were scored on day 3 and day 5 as excellent, good, moderate or poor based on Alpha/ESHRE guidelines and Gardner and Schoolcraft scoring-system. If embryos changed category between day 3 and 5, the number of steps (between excellent; good; moderate; poor) in positive and negative direction was expressed. RESULTS: On day 5, the ongoing pregnancy rate (OPR) of excellent embryos was 37.4 %. Univariate analyses showed that on day 5, both a higher cell stage, better inner cell mass and better trophectoderm were significantly associated with an ongoing pregnancy. In case of deterioration in quality of individual embryos between day 3 and day 5, the OPR was significantly lower. Conversely, improvement of embryo quality between day 3 and day 5 showed higher ongoing pregnancy rates (overall OPR of good day-3 embryos improving to excellent day-5 embryos: 30 %; moderate day 3 to excellent day 5: 50 %; poor day 3 to excellent day 5: 42 %; poor day 3 to good day 5: 20 %; poor day 3 to moderate day 5: 16 %). When embryos improved from poor on day 3 to excellent day 5 the OPR was significantly higher in comparison with embryos that did not change in quality scoring during development (steady embryos) (OR: 1.785, p < 0.05). CONCLUSION: Our results suggest that it is more likely to achieve an ongoing pregnancy when transferring an embryo that has improved in quality between days 3 and 5 as opposed to one that has remained stable.

Synthetic Elastography Using B-Mode Ultrasound Through a Deep Fully Convolutional Neural Network.

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently developed, advanced technique assesses the speed of a laterally traveling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5 ± 0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t -distributed stochastic neighbor embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at the development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility.

Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications.

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Visual inspection of transvaginal ultrasound videos to characterize uterine peristalsis: an inter-observer agreement study.

PURPOSE: Contractions in non-pregnant uterine can be assessed by visual inspection of transvaginal ultrasound (TVUS). Many authors have used this method to extract features like contraction frequency and direction. However, visual inspection is a subjective method and the outcome is dependent on the sonographers and video analysts. In this study, we wanted to see which uterine feature is reproducible enough, in terms of inter-observer agreement, to serve as a reliable control for future research. METHODS: Six observers assessed 80 TVUS videos, and rated video quality, contraction frequency, direction and timing. One observer assessed operating time. A Fleiss' kappa (κ) or an intra-class correlation (ICC) was calculated to determine the inter-observer agreement of all features. RESULTS: The inter-observer agreement in frequency was substantial (ICC = 0.68). Conversely, there was just slight to fair agreement in contraction timing and direction and in video quality: ICC = 0.26, κ = 0.17 and κ = 0.16, respectively. Overall, agreement among technical engineers was better than between medical professionals. The level of agreement was correlated with video quality, phase of the menstrual cycle and individual patient (all χ2 with p < 0.00). The time to analyze one video ranged between 6 and 20 min. CONCLUSIONS: This study shows that visual inspection of TVUS videos is a fairly reproducible method to assess contraction frequency. However, the operating time is too extensive to implement this method in daily practice. Automated methods could offer a solution for this problem in the future.

Investigation of The Neural Drive During Vibration Exercise by High-density Surface-electromyography.

Mechanical vibration applied directly to the muscle belly or tendon has been reported to elicit a specific reflex loop named tonic vibration reflex (TVR), which involves motor unit (MU) activation synchronized and un-synchronized within the vibration cycle. Indirect application of vibration to the muscle by vibration exercise (VE) has also been suggested to evoke TVR, as evidenced by the spectral peaks observed at the vibration frequency in the surface electromyography (sEMG). However, other studies interpreted these spectral peaks as the result of motion artifacts (MAs). The aim of the present study is, therefore, to investigate MU activation patterns during VE in order to clarify the nature of those spectral peaks. To this end, low-intensity isometric contractions were executed with and without VE, and high-density sEMG measurements were performed during the contraction tasks. MU action potential (MUAP) trains were extracted by decomposing the recorded high-density sEMG signals. The spectra of the MUAP trains were then calculated and compared between vibration and no-vibration conditions. Clear MU synchronization was observed during VE, confirming the spectral peaks at the vibration frequency to be mainly due to the reflex loop rather than MAs.

Convective-Dispersion Modeling in 3D Contrast-Ultrasound Imaging for the Localization of Prostate Cancer.

Despite being the solid tumor with the highest incidence in western men, prostate cancer (PCa) still lacks reliable imaging solutions that can overcome the need for systematic biopsies. Dynamic contrast-enhanced ultrasound imaging (DCE-US) allows us to quantitatively characterize the vascular bed in the prostate, due to its ability to visualize an intravenously administered bolus of contrast agents. Previous research has demonstrated that DCE-US parameters related to the vascular architecture are useful markers for the localization of PCa lesions. In this paper, we propose a novel method to assess the convective dispersion (D) and velocity (v) of the contrast bolus spreading through the prostate from three-dimensional (3D) DCE-US recordings. By assuming that D and v are locally constant, we solve the convective-dispersion equation by minimizing the corresponding regularized least-squares problem. 3D multiparametric maps of D and v were compared with 3D histopathology retrieved from the radical prostatectomy specimens of six patients. With a pixel-wise area under the receiver operating characteristic curve of 0.72 and 0.80, respectively, the method shows diagnostic value for the localization of PCa.

Effects of vibration-induced fatigue on the H-reflex.

Vibration exercise (VE) has been suggested as an effective training for improving muscle strength and coordination. However, the underlying physiological adaptation processes are not yet fully understood, limiting the development of safe and effective exercise protocols. To better understand the neuromuscular responses elicited by VE, we aimed at investigating the acute effects of superimposed vibration on the Hoffmann reflex (H-reflex), measured after fatiguing exercise. Twenty-five volunteers performed four isometric contractions of the right Flexor Carpi Radialis (FCR) with baseline load at 80% of their maximal voluntary contraction (MVC), both with no vibration and with superimposed vibration at 15, 30, and 45 Hz. Fatigue was estimated by MVC test and estimation of electromyographic spectral compression. H-reflex suppression was estimated as the relative decrease after exercise. Our results show that fatiguing exercise determined a decrease in H-reflex amplitude compared to rest condition while vibration determined a lower H-reflex suppression as compared to no vibration. The superimposition of 30-Hz vibration determined the largest acute reduction in force generating capacity (36 N, p < 0.05) and the lowest H-reflex suppression (20%, p < 0.05). These results suggest VE to be particularly suitable in rehabilitation programs for rapid restoration of muscle form and function after immobilization periods.