Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution

Postural deformities often manifest themselves in a sagittal imbalance and an asymmetric morphology of the torso. As a novel topographic method, torsobarography assesses the morphology of the back by analysing pressure distribution along the torso in a lying position. At torsobarography's core is a capacitive pressure sensor array. To evaluate its feasibility as a diagnostic tool, the reproducibility of the system and extracted anatomical associated parameters were evaluated on 40 subjects. Landmarks and reference distances were identified within the pressure images. The examined parameters describe the shape of the spine, various structures of the trunk symmetry, such as the scapulae, and the pelvic posture. The results showed that the localisation of the different structures performs with a good (ICC > 0.75) to excellent (ICC > 0.90) reliability. In particular, parameters for approximating the sagittal spine shape were reliably reproduced (ICC > 0.83). Lower reliability was observed for asymmetry parameters, which can be related to the low variability within the subject group. Nonetheless, the reliability levels of selected parameters are comparable to commercial systems. This study demonstrates the substantial potential of torsobarography at its current stage for reliable posture analysis and may pave the way as an early detection system for postural deformities.

Assessment of the human response to acute mental stress-An overview and a multimodal study

Numerous vital signs are reported in association with stress response assessment, but their application varies widely. This work provides an overview over methods for stress induction and strain assessment, and presents a multimodal experimental study to identify the most important vital signs for effective assessment of the response to acute mental stress. We induced acute mental stress in 65 healthy participants with the Mannheim Multicomponent Stress Test and acquired self-assessment measures (Likert scale, Self-Assessment Manikin), salivary α-amylase and cortisol concentrations as well as 60 vital signs from biosignals, such as heart rate variability parameters, QT variability parameters, skin conductance level, and breath rate. By means of statistical testing and a self-optimizing logistic regression, we identified the most important biosignal vital signs. Fifteen biosignal vital signs related to ventricular repolarization variability, blood pressure, skin conductance, and respiration showed significant results. The logistic regression converged with QT variability index, left ventricular work index, earlobe pulse arrival time, skin conductance level, rise time and number of skin conductance responses, breath rate, and breath rate variability (F1 = 0.82). Self-assessment measures indicated successful stress induction. α-amylase and cortisol showed effect sizes of -0.78 and 0.55, respectively. In summary, the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system were successfully activated. Our findings facilitate a coherent and integrative understanding of the assessment of the stress response and help to align applications and future research concerning acute mental stress.

Approach for Non-Intrusive Detection of the Fit of Orthopaedic Devices Based on Vibrational Data

The soft tissues of residual limb amputees are subject to large volume fluctuations over the course of a day. Volume fluctuations in residual limbs can lead to local pressure marks, causing discomfort, pain and rejection of prostheses. Existing methods for measuring interface stress encounter several limitations. A major problem is that the measurement instrumentation is applied in the sensitive interface between the prosthesis and residual limb. This paper presents the principle investigation of a non-intrusive technique to evaluate the fit of orthopaedic prosthesis sockets in transfemoral amputees based on experimentally obtained vibrational data. The proposed approach is based on changes in the dynamical behaviour detectable at the outer surface of prostheses; thus, the described interface is not affected. Based on the experimental investigations shown and the derived results, it can be concluded that structural dynamic measurements are a promising non-intrusive technique to evaluate the fit of orthopaedic prosthesis sockets in transfemoral amputee patients. The obtained resonance frequency changes of 2% are a good indicator of successful applicabilityas these changes can be detected without the need for complex measurement devices.

Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients.

A Wireless Rowing Measurement System for Improving the Rowing Performance of Athletes

The rowing technique is a key factor in the overall rowing performance. Nowadays the athletes' performance is so advanced that even small differences in technique can have an impact on sport competitions. To further improve the athletes' performance, individualized rowing is necessary. This can be achieved by intelligent measurement technology that provides direct feedback. To address this issue, we developed a novel wireless rowing measurement system (WiRMS) that acquires rowing movement and measures muscle activity using electromyography (EMG). Our measurement system is able to measure several parameters simultaneously: the rowing forces, the pressure distribution on the scull, the oar angles, the seat displacement and the boat acceleration. WiRMS was evaluated in a proof-of-concept study with seven experienced athletes performing a training on water. Evaluation results showed that WiRMS is able to assess the rower's performance by recording the rower's movement and force applied to the scull. We found significant correlations (p < 0.001) between stroke rate and drive-to-recovery ratio. By incorporating EMG data, a precise temporal assignment of the activated muscles and their contribution to the rowing motion was possible. Furthermore, we were able to show that the rower applies the force to the scull mainly with the index and middle fingers.

Detection of Fall Risk in Multiple Sclerosis by Gait Analysis-An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms

One of the common causes of falls in people with Multiple Sclerosis (pwMS) is walking impairment. Therefore, assessment of gait is of importance in MS. Gait analysis and fall detection can take place in the clinical context using a wide variety of available methods. However, combining these methods while using machine learning algorithms for detecting falls has not been performed. Our objective was to determine the most relevant method for determining fall risk by analyzing eleven different gait data sets with machine learning algorithms. In addition, we examined the most important features of fall detection. A new feature selection ensemble (FS-Ensemble) and four classification models (Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, Support Vector Machine) were used. The FS-Ensemble consisted of four filter methods: Chi-square test, information gain, Minimum Redundancy Maximum Relevance and RelieF. Various thresholds (50%, 25% and 10%) and combination methods (Union, Union 2, Union 3 and Intersection) were examined. Patient-reported outcomes using specialized walking questionnaires such as the 12-item Multiple Sclerosis Walking Scale (MSWS-12) and the Early Mobility Impairment Questionnaire (EMIQ) achieved the best performances with an F1 score of 0.54 for detecting falls. A combination of selected features of MSWS-12 and EMIQ, including the estimation of walking, running and stair climbing ability, the subjective effort as well as necessary concentration and walking fluency during walking, the frequency of stumbling and the indication of avoidance of social activity achieved the best recall of 75%. The Gaussian Naive Bayes was the best classification model for detecting falls with almost all data sets. FS-Ensemble improved the classification models and is an appropriate technique for reducing data sets with a large number of features. Future research on other risk factors, such as fear of falling, could provide further insights.

Investigation of muscle fatigue during on-water rowing using surface EMG

In this work, four different algorithms (fast Fourier transform FFT, short-time Fourier transform STFT, continuous wavelet transform CWT, and instantaneous frequency IF) for calculating median frequency (MDF) from surface EMG signals were investigated for studying muscle fatigue during a on-water rowing training. The study protocol included 5 consecutive parts with increasing stroke rate. Six athletes participated in the study aged 36.6+-14.6 years and a rowing experience of 6 to 35 years. We considered eight muscles: biceps brachii right, biceps brachii left, latissimus dorsi right, latissimus dorsi left, erector spinae right, erector spinae left, rectus femoris and biceps femoris. By applying Friedmann test, we found a significant difference in MDF behavior between algorithms in assessing muscle fatigue . Correlation analyses showed significant correlations between muscle activity duration t_act and MDF, which differs for the four considered algorithms and should be taken into account in further experiments. With CWT showing the smallest correlation to t_act it might be more robust against time window variations. Our study provides a basis for the development of improved methods for more robust, non-invasive, and continuous detection of muscle fatigue in experiments with dynamic on-water rowing study designs.

Camera-based assessment of cutaneous perfusion strength in a clinical setting

Objective. After skin flap transplants, perfusion strength monitoring is essential for the early detection of tissue perfusion disorders and thus to ensure the survival of skin flaps. Camera-based photoplethysmography (cbPPG) is a non-contact measurement method, using video cameras and ambient light, which provides spatially resolved information about tissue perfusion. It has not been researched yet whether the measurement depth of cbPPG, which is limited by the penetration depth of ambient light, is sufficient to reach pulsatile vessels and thus to measure the perfusion strength in regions that are relevant for skin flap transplants.Approach. We applied constant negative pressure (compared to ambient pressure) to the anterior thighs of 40 healthy subjects. Seven measurements (two before and five up to 90 minutes after the intervention) were acquired using an RGB video camera and photospectrometry simultaneously. We investigated the performance of different algorithmic approaches for perfusion strength assessment, including the signal-to-noise ratio (SNR), its logarithmic components logS and logN, amplitude maps, and the amplitude height of alternating and direct signal components.Main results. We found strong correlations of up tor=0.694 (p<0.001) between photospectrometric measurements and all cbPPG parameters except SNR when using the green color channel. The transfer of cbPPG signals to POS, CHROM, and O3C did not lead to systematic improvements. However, for direct signal components, the transformation to O3C led to correlations of up tor=0.744 (p<0.001) with photospectrometric measurements.Significance. Our results indicate that a camera-based perfusion strength assessment in tissue with deep-seated pulsatile vessels is possible.

Hyperspectral Imaging during Normothermic Machine Perfusion—A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks

Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550–995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.

Pulse Arrival Time - A Sensitive Vital Parameter for the Detection of Mental Stress

Mental stress triggers positive inotropic and chronotropic effects as well as peripheral vasoconstriction. This alters the pulse arrival time (PAT), the duration between electrical excitation of the ventricles and arrival of the pulse wave in the periphery. We conducted a study to examine PAT during five rest blocks and under mental stress utilizing the Mannheim Multicomponent Stress Test. Electrocardiograms as well as finger and earlobe photoplethysmograms were recorded. PAT was calculated for over 135,000 heartbeats from 42 healthy volunteers as the time duration between the R peak in the electrocardiogram and the following pulse onset in the respective photoplethysmogram. To identify the effect of mental stress, block-wise PAT means were statistically analyzed with repeated measures ANOVA. The analyses showed significant differences between the block means for both PAT measures (p < 0.001). Post-hoc tests revealed significantly reduced PAT during the stress block compared to all rest blocks for both PAT measures (p < 0.001). We found no significant differences between the rest blocks. Our results support that PAT is a sensitive vital parameter for the detection of mental stress in healthy volunteers. This holds true for both measurement positions, the finger and the earlobe.

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (κ = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (κ = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

Comparison of different spectral cameras for image-guided organ transplantation

SIGNIFICANCE

Hyperspectral and multispectral imaging (HMSI) in medical applications provides information about the physiology, morphology, and composition of tissues and organs. The use of these technologies enables the evaluation of biological objects and can potentially be applied as an objective assessment tool for medical professionals.

AIM

Our study investigates HMSI systems for their usability in medical applications.

APPROACH

Four HMSI systems (one hyperspectral pushbroom camera and three multispectral snapshot cameras) were examined and a spectrometer was used as a reference system, which was initially validated with a standardized color chart. The spectral accuracy of the cameras reproducing chemical properties of different biological objects (porcine blood, physiological porcine tissue, and pathological porcine tissue) was analyzed using the Pearson correlation coefficient.

RESULTS

All the HMSI cameras examined were able to provide the characteristic spectral properties of blood and tissues. A pushbroom camera and two snapshot systems achieve Pearson coefficients of at least 0.97 compared to the ground truth, indicating a very high positive correlation. Only one snapshot camera performs moderately to high positive correlation (0.59 to 0.85).

CONCLUSION

The knowledge of the suitability of HMSI cameras for accurate measurement of chemical properties of biological objects offers a good opportunity for the selection of the optimal imaging tool for specific medical applications, such as organ transplantation.

Instantaneous Cardiac Baroreflex Sensitivity: xBRS Method Quantifies Heart Rate Blood Pressure Variability Ratio at Rest and During Slow Breathing

Spontaneous baroreflex sensitivity (BRS) is a widely used tool for the quantification of the cardiovascular regulation. Numerous groups use the xBRS method, which calculates the cross-correlation between the systolic beat-to-beat blood pressure and the R-R interval (resampled at 1 Hz) in a 10 s sliding window, with 0-5 s delays for the interval. The delay with the highest correlation is selected and, if significant, the quotient of the standard deviations of the R-R intervals and the systolic blood pressures is recorded as the corresponding xBRS value. In this paper we test the hypothesis that the xBRS method quantifies the causal interactions of spontaneous BRS from non-invasive measurements at rest. We use the term spontaneous BRS in the sense of the sensitivity curve is calculated from non-interventional, i.e., spontaneous, baroreceptor activity. This study includes retrospective analysis of 1828 measurements containing ECG as well as continues blood pressure under resting conditions. Our results show a high correlation between the heart rate - systolic blood pressure variability (HRV/BPV) quotient and the xBRS (r = 0.94, p < 0.001). For a deeper understanding we conducted two surrogate analyses by substituting the systolic blood pressure by its reversed time series. These showed that the xBRS method was not able to quantify causal relationships between the two signals. It was not possible to distinguish between random and baroreflex controlled sequences. It appears xBRS rather determines the HRV/BPV quotient. We conclude that the xBRS method has a potentially large bias in characterizing the capacity of the arterial baroreflex under resting conditions. During slow breathing, estimates for xBRS are significantly increased, which clearly shows that measurements at rest only involve limited baroreflex activity, but does neither challenge, nor show the full range of the arterial baroreflex regulatory capacity. We show that xBRS is exclusively dominated by the heart rate to systolic blood pressure ratio (r = 0.965, p < 0.001). Further investigations should focus on additional autonomous testing procedures such as slow breathing or orthostatic testing to provide a basis for a non-invasive evaluation of baroreflex sensitivity.

Neighborhood Optimization for Therapy Decision Support

This work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.

Predicting sepsis with a recurrent neural network using the MIMIC III database

OBJECTIVE

Predicting sepsis onset with a recurrent neural network and performance comparison with InSight - a previously proposed algorithm for the prediction of sepsis onset.

METHODOLOGY

A retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC III database) who did not fall under the definition of sepsis at the time of admission. The area under the receiver operating characteristic (AUROC) measures the performance of the prediction task. We examine the sequence length given to the machine learning algorithms for different points in time before sepsis onset concerning the prediction performance. Additionally, the impact of sepsis onset's definition is investigated. We evaluate the model with a relatively large and thus more representative patient population compared to related works in the field.

RESULTS

For a prediction 3 h prior to sepsis onset, our network achieves an AUROC of 0.81 (95% CI: 0.78-0.84). The InSight algorithm achieves an AUROC of 0.72 (95% CI: 0.69-0.75). For a fixed sensitivity of 90% our network reaches a specificity of 47.0% (95% CI: 43.1%-50.8%) compared to 31.1% (95% CI: 24.8%-37.5%) for InSight. In addition, we compare the performance for 6 and 12 h prediction time for both approaches.

CONCLUSION

Our findings demonstrate that a recurrent neural network is superior to InSight considering the prediction performance. Most probably, the improvement results from the network's ability of revealing time dependencies. We show that the length of the look back has a significant impact on the performance of the classifier. We also demonstrate that for the correct detection of sepsis onset for a retrospective analysis, further research is necessary.

The FED-Method (Fixation, Elongation, Derotation) - a Machine-supported Treatment Approach to Patients with Idiopathic Scoliosis - Systematic Review

BACKGROUND

The FED method (Fixation, Elongation, Derotation) is a treatment method approach to Patients with scoliosis. The FED method is especially established in Spain and Poland, whereby in Germany it is less well-known. Nevertheless the FED method is within the scope of a research project (Project Number: 19200 BR/3). The purpose of the paper is to characterize the FED method and to highlight the specificities in contrast to the Schroth method, which is international established and especially in Germany.

METHODS

This systematic literature research was conducted in Nov 2017-Jan 2018. Therefore common medical and physiotherapeutic databases were used. Furthermore there was a hand search in selected scientific journals. Only a small number of relevant references were identified. That is why the respective authors were asked to provide the full-texts of their papers and to recommend further references.

RESULTS

A total of 378 references were identified. After removing duplicates and the content-related selection, 19 references were deemed to be relevant. Based on the analysis of this relevant literature, the FED method was comprehensively characterized. First of all the general structure of the FED method and the scientific evidence for its effectiveness was described. And as a result of the literature research, the operating principles of the FED method were pointed out. Then these operating principles were discussed in comparison with the Schroth method. The Schroth method based on sensomotoric and kinesthetic principles and the correction of the pathologic posture was performed by selective muscle activation and breathing-pattern. Thus, the posture correction will be performed by the patients (auto correction). Compared to the Schroth method, the FED method implements the posture correction by the FED-device. This correction is influenced by mechanical forces with a comparatively high strength and intensity. The repetitive mechanical correction stimulates the sensomotoric system. And due to trophic/biochemical adaptations, the physiological bone growth will be stimulated.

CONCLUSION

In total the authors want to clarify, that both treatment methods (Schroth method, FED method) supposed to be applied in consideration of the preconditions of the patients and the pursue of the different treatment goals. Thus, the implementation of treatment methods should be used according to the individual treatment demand and on different stages in the treatment process.

Algorithms for mapping kidney tissue oxygenation during normothermic machine perfusion using hyperspectral imaging

The lack of donor grafts is a severe problem in transplantation medicine. Hence, the improved preservation of existing and the usage of organs that were deemed untransplantable is as urgent as ever. The development of novel preservation techniques has come into focus. A promising alternative to traditional cold storage is normothermic machine perfusion (NMP), which provides the benefit of improving the organs' viability and of assessing the organs' status under physiological conditions. For this purpose, methods for evaluating organ parameters have yet to be developed. In a previous study, we determined the tissue oxygen saturation (StO2) of kidneys during NMP with hyperspectral imaging (HSI) based on a discrete wavelength (DW) algorithm. The aim of the current study was to identify a more accurate algorithm for StO2 calculation. A literature search revealed three candidates to test: a DW algorithm and two full spectral algorithms - area under a curve and partial least square regression (PLSR). After obtaining suitable calibration data to train each algorithm, they were evaluated during NMP. The wavelength range from 590 to 800 nm was found to be appropriate for analyzing StO2 of kidneys during NMP. The PLSR method shows good results in analyzing the tissues' oxygen status in perfusion experiments.

Cardiovascular assessment by imaging photoplethysmography - a review

Over the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique's background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.

Nocturnal ventricular repolarization lability predicts cardiovascular mortality in the Sleep Heart Health Study

The objective of the present study was to quantify repolarization lability and its association with sex, sleep stage, and cardiovascular mortality. We analyzed polysomnographic recordings of 2,263 participants enrolled in the Sleep Heart Health Study (SHHS-2). Beat-to-beat QT interval variability (QTV) was quantified for consecutive epochs of 5 min according to the dominant sleep stage [wakefulness, nonrapid eye movement stage 2 (NREM2), nonrapid eye movement stage 3 (NREM3), and rapid eye movement (REM)]. To explore the effect of sleep stage and apnea-hypopnea index (AHI) on QT interval parameters, we used a general linear mixed model and mixed ANOVA. The Cox proportional hazards model was used for cardiovascular disease (CVD) death prediction. Sex-related differences in T wave amplitude ( P < 0.001) resulted in artificial QTV differences. Hence, we corrected QTV parameters by T wave amplitude for further analysis. Sleep stages showed a significant effect ( P < 0.001) on QTV. QTV was decreased in deep sleep compared with wakefulness, was higher in REM than in NREM, and showed a distinct relation to AHI in all sleep stages. The T wave amplitude-corrected QTV index (cQTVi) in REM sleep was predictive of CVD death (hazard ratio: 2.067, 95% confidence interval: 1.105–3.867, P < 0.05) in a proportional hazards model. We demonstrated a significant impact of sleep stages on ventricular repolarization variability. Sex differences in QTV are due to differences in T wave amplitude, which should be corrected for. Independent characteristics of QTV measures to sleep stages and AHI showed different behaviors of heart rate variability and QTV expressed as cQTVi. cQTVi during REM sleep predicts CVD death.

NEW & NOTEWORTHY We demonstrate here, for the first time, a significant impact of sleep stages on ventricular repolarization variability, quantified as QT interval variability (QTV). We showed that QTV is increased in rapid eye movement sleep, reflective of high sympathetic drive, and predicts death from cardiovascular disease. Sex-related differences in QTV are shown to be owing to differences in T wave amplitude, which should be corrected for.

Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation

OBJECTIVE

Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy.

METHODS

Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component.

RESULTS

The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats.

CONCLUSION

Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia.

SIGNIFICANCE

The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.

Camera-based photoplethysmography in an intraoperative setting

Background

Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment.

Methods

We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity.

Results

The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations.

Conclusions

The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0467-7) contains supplementary material, which is available to authorized users.

Additively Manufactured Pneumatically Driven Skin Electrodes

Telemedicine focuses on improving the quality of health care, particularly in out-of-hospital settings. One of the most important applications is the continuous remote monitoring of vital parameters. Long-term monitoring of biopotentials requires skin-electrodes. State-of-the-art electrodes such as Ag/AgCl wet electrodes lead, especially during long-term application, to complications, e.g., skin irritations. This paper presents a low-cost, on-demand electrode approach for future long-term applications. The fully printed module comprises a polymeric substrate with electrodes on a flexible membrane, which establishes skin contact only for short time in case of measurement. The membranes that produce airtight seals for pressure chambers can be pneumatically dilated and pressed onto the skin to ensure good contact, and subsequently retracted. The dilatation depends on the pressure and membrane thickness, which has been tested up to 150 kPa. The electrodes were fabricated in screen and inkjet printing technology, and compared during exemplary electrodermal activity measurement (EDA). The results show less amplitude compared to conventional EDA electrodes but similar behavior. Because of the manufacturing process the module enables high individuality for future applications.

Relation between pulse pressure and the pulsation strength in camera-based photoplethysmograms

Camera-based photoplethysmography (cbPPG) is an innovative measuring technique that enables the remote extraction of vital signs using video cameras. Most studies in the field focus on heart rate detection while other physiological quantities are often ignored. In this work, we analyzed the relation between the pulse pressure and the pulsation strengths of cbPPG signals for 70 patients after surgery. Our results show a high correlation between the two measures (r = 0.54). Furthermore, the influence of technical and medical factors was tested. The controlled impact of these factors proved to enhance the correlation by between 9 and 27 %.

The value of polarization in camera-based photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measuring technique that permits the remote acquisition of cardiovascular signals using video cameras. Research still lacks in fundamental studies to reach a deeper technical and physiological understanding. This work analyzes the employment of polarization filtration to (i) assess the gain for the signal quality and (ii) draw conclusions about the cbPPG signal's origin. We evaluated various forehead regions of 18 recordings with different color and filter settings. Our results prove that for an optimal illumination, the perpendicular filter setting provides a significant benefit. The outcome supports the theory that signals arise from blood volume changes. For lateral illumination, ballistocardiographic effects dominate the signal as polarization's impact vanishes.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Therapy Decision Support Based on Recommender System Methods

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation

OBJECTIVE

The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation.

METHODS

Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used.

RESULTS

The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy.

CONCLUSION

The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation.

SIGNIFICANCE

NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.

Assessment of blind source separation techniques for video-based cardiac pulse extraction

Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.

Camera-based photoplethysmography in critical care patients

BACKGROUND

Camera-based photoplethysmography (cbPPG) is an optical measurement technique that reveals pulsatile blood flow in cutaneous microcirculation from a distance. cbPPG has been shown to reflect pivotal haemodynamic events like cardiac ejection in healthy subjects. In addition, it provides valuable insight into intrinsic microcirculatory regulation as it yields dynamic, two-dimensional perfusion maps. In this study, we evaluate the feasibility of a clinical cbPPG application in critical care patients.

METHODS

A mobile camera set-up to record faces of patients at the bed site was constructed. Videos were made during the immediate recovery after cardiac surgery under standard critical care conditions and were processed offline. Major motion artefacts were detected using an optical flow technique and suitable facial regions were manually annotated. cbPPG signals were highpass filtered and Fourier spectra out of consecutive 10s signal segments calculated for heart rate detection. Signal-to-noise ratios (SNR) of the Fourier spectra were derived as a quality measure. Reference data of vital parameters were synchronously acquired from the bed site monitoring system.

RESULTS

Seventy patient videos of an average time of 28.6±2.8 min were analysed. Heart rate (HR) was detected within a±5 bpm range compared to reference in 83% of total recording time. Low SNR and HR detection failure were mostly, but not exclusively, attributed to non-physiological events like patient motion, interventions or sudden changes of illumination. SNR was reduced by low arterial blood pressure, whereas no impact of other perioperative or disease-related parameters was identified.

CONCLUSION

Cardiac ejection is detectable by cbPPG under pathophysiologic conditions of cardiovascular disease and perioperative medicine. cbPPG measurements can be seamlessly integrated into the clinical work flow of critical care patients.

T Wave Amplitude Correction of QT Interval Variability for Improved Repolarization Lability Measurement

Objectives: The inverse relationship between QT interval variability (QTV) and T wave amplitude potentially confounds QT variability assessment. We quantified the influence of the T wave amplitude on QTV in a comprehensive dataset and devised a correction formula.

Methods: Three ECG datasets of healthy subjects were analyzed to model the relationship between T wave amplitude and QTV. To derive a generally valid correction formula, linear regression analysis was used. The proposed correction formula was applied to patients enrolled in the Evaluation of Defibrillator in Non-Ischemic Cardiomyopathy Treatment Evaluation trial (DEFINITE) to assess the prognostic significance of QTV for all-cause mortality in patients with non-ischemic dilated cardiomyopathy.

Results: A strong inverse relationship between T wave amplitude and QTV was demonstrated, both in healthy subjects (R² = 0.68, p < 0.001) and DEFINITE patients (R² = 0.20, p < 0.001). Applying the T wave amplitude correction to QTV achieved 2.5-times better group discrimination between patients enrolled in the DEFINITE study and healthy subjects. Kaplan-Meier estimator analysis showed that T wave amplitude corrected QTVi is inversely related to survival (p < 0.01) and a significant predictor of all-cause mortality.

Conclusion: We have proposed a simple correction formula for improved QTV assessment. Using this correction, predictive value of QTV for all-cause mortality in patients with non-ischemic cardiomyopathy has been demonstrated.

The effect of body posture on cognitive performance: a question of sleep quality

Nearly all functional magnetic resonance imaging (fMRI) studies are conducted in the supine body posture, which has been discussed as a potential confounder of such examinations. The literature suggests that cognitive functions, such as problem solving or perception, differ between supine and upright postures. However, the effect of posture on many cognitive functions is still unknown. Therefore, the aim of the present study was to investigate the effects of body posture (supine vs. sitting) on one of the most frequently used paradigms in the cognitive sciences: the N-back working memory paradigm. Twenty-two subjects were investigated in a randomized within-subject design. Subjects performed the N-back task on two consecutive days in either the supine or the upright posture. Subjective sleep quality and chronic stress were recorded as covariates. Furthermore, changes in mood dimensions and heart rate variability (HRV) were assessed during the experiment. Results indicate that the quality of sleep strongly affects reaction times when subjects performed a working memory task in a supine posture. These effects, however, could not be observed in the sitting position. The findings can be explained by HRV parameters that indicated differences in autonomic regulation in the upright vs. the supine posture. The finding is of particular relevance for fMRI group comparisons when group differences in sleep quality cannot be ruled out.

Effects of awareness and nociception on heart rate variability during general anaesthesia

During anaesthesia awareness and nociception are serious complications that may further lead to haemodynamic instability. Specific monitoring of depth of hypnosis and depth of analgesia based on heart rate variability (HRV) analysis is eligible to improve patient safety and reduce efforts in post-operative care. Consequently, in this analysis we assess the applicability of HRV parameters during surgical interventions with standardized intravenous propofol-remifentanil-anaesthesia. Peri-operative electrocardiograms were recorded from cardiovascular stable patients (ASA Score I/II, N = 32, age: 36.4 ± 11.23 a, BMI: 25.2 ± 3.16) scheduled for trauma and dentofacial surgery. HRV time- and frequency-domain parameters, measures of complexity and nonlinear dynamics were compared by analysing longitudinally distributed 300 s intervals preceding/following induction of anaesthesia (BL-I1), intubation (I1-I2) and extubation (E1-E2). Mean value (meanNN) and standard deviation (sdNN) of the heart rate are influenced in BL-I1 (p < 0.001), I1-I2 (p < 0.05) and E1-E2 (p < 0.001). The number of forbidden words of symbolic dynamics changes significantly for BL-I1 (p < 0.001) and not for I1-I2 and E1-E2 (p > 0.05). Probability of low-variability POLVAR10 is significantly altered in all comparisons (BL-I1: Δ = 0.032, p < 0.01, I1-I2: Δ = 0.12, p < 0.05, E1-E2: Δ = 0.169, p < 0.01) but especially during nociception. While standard time-domain parameters lacked selectivity, parameters of symbolic dynamics appear to be specifically influenced by changes in depth of hypnosis and nociception, respectively. However, the lack of steady-state ventilation/breathing in this study needs to be considered in future research. To be used for clinical anaesthesia monitoring our results have to be prospectively validated in clinical studies.