Facial thermal response to non-painful stressor in premature and term neonates.

BACKGROUND: This study is a preliminary clinical investigation with the objective to evaluate the facial thermal response of premature and term neonates to a non-painful stressor (hunger) using infrared thermography (IRT). The development of objective and reliable parameters to monitor pain and stress is of relevance for optimal neonatal outcome and achieving a better management of patient comfort. METHODS: We enrolled 12 neonates ranging from 27 to 39 weeks gestation (median: 34) and aged 3-79 days (median: 13). Recordings were performed before and after feeding, with and without hunger. Six regions of interest were chosen for evaluation (nose tip, periorbital and corrugator region, forehead, perioral and chin region). RESULTS: There was an increase in the facial temperature in infants immediately prior to their next feed relative to infants who were not hungry, with the nasal tip being the facial evaluation site with the greatest temperature change. CONCLUSIONS: The IRT appears to be a feasible and suitable method to detect changes in the neonatal patient. The thermal variations observed seem to reflect an arousal mediated by the parasympathetic nervous system, which has been described in existing infant stress research. IMPACT: This is the first study to examine the use of infrared thermography (IRT) in monitoring the facial thermal response to a mild stressor (hunger) in premature and term neonates. Hunger as a mild, non-pain-associated stressor showed a significant effect on the facial temperature. The thermal signature of the regions of interest chosen showed hunger-related thermal variations. Results suggest the feasibility and suitability of IRT as an objective diagnostic tool to approach stress and changes in the condition of the neonatal patient.

Multi-modal body part segmentation of infants using deep learning.

BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis. Thermography may be a non-contact and wireless alternative to state-of-the-art, cable-based methods. For monitoring use in clinical practice, automatic segmentation of the different body regions is necessary due to the movement of the infant. METHODS: This work presents and evaluates algorithms for automatic segmentation of infant body parts using deep learning methods. Based on a U-Net architecture, three neural networks were developed and compared. While the first two only used one imaging modality (visible light or thermography), the third applied a feature fusion of both. For training and evaluation, a dataset containing 600 visible light and 600 thermography images from 20 recordings of infants was created and manually labeled. In addition, we used transfer learning on publicly available datasets of adults in combination with data augmentation to improve the segmentation results. RESULTS: Individual optimization of the three deep learning models revealed that transfer learning and data augmentation improved segmentation regardless of the imaging modality. The fusion model achieved the best results during the final evaluation with a mean Intersection-over-Union (mIoU) of 0.85, closely followed by the RGB model. Only the thermography model achieved a lower accuracy (mIoU of 0.75). The results of the individual classes showed that all body parts were well-segmented, only the accuracy on the torso is inferior since the models struggle when only small areas of the skin are visible. CONCLUSION: The presented multi-modal neural networks represent a new approach to the problem of infant body segmentation with limited available data. Robust results were obtained by applying feature fusion, cross-modality transfer learning and classical augmentation strategies.

SOLVe: a closed-loop system focused on protective mechanical ventilation.

BACKGROUND: Mechanical ventilation is an essential component in the treatment of patients with acute respiratory distress syndrome. Prompt adaptation of the settings of a ventilator to the variable needs of patients is essential to ensure personalised and protective ventilation. Still, it is challenging and time-consuming for the therapist at the bedside. In addition, general implementation barriers hinder the timely incorporation of new evidence from clinical studies into routine clinical practice. RESULTS: We present a system combing clinical evidence and expert knowledge within a physiological closed-loop control structure for mechanical ventilation. The system includes multiple controllers to support adequate gas exchange while adhering to multiple evidence-based components of lung protective ventilation. We performed a pilot study on three animals with an induced ARDS. The system achieved a time-in-target of over 75 % for all targets and avoided any critical phases of low oxygen saturation, despite provoked disturbances such as disconnections from the ventilator and positional changes of the subject. CONCLUSIONS: The presented system can provide personalised and lung-protective ventilation and reduce clinician workload in clinical practice.

Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset.

In today's neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images' effect on the adversarial network's generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.

Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation.

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver's Vital Signs.

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.

Detection of acute ventilatory problems via magnetic induction in a newborn animal model.

BACKGROUND: Magnetic induction measurement (MIM) is a noninvasive method for the contactless registration of respiration in newborn piglets by using measurement coils positioned at the bottom of an incubator. Acute pulmonary problems may be determinants of poor neurological and psychomotor outcomes in preterm infants. The current study tested the detection of pulmonary ventilation disorders via MIM in 11 newborn piglets. METHODS: Six measurement coils determined changes in magnetic induction, depending on the ventilation of the lung, in comparison with flow resistance. Contactless registration of induced acute pulmonary ventilation disorders (apnea, atelectasis, pneumothorax, and aspiration) was detected by MIM. RESULTS: All pathologies except aspiration were detected by MIM. Significant changes occurred after induction of apnea (three coils), malposition of the tube (one coil), and pneumothorax (three coils) (p ≤ 0.05). No significant changes occurred after induction of aspiration (p = 0.12). CONCLUSIONS: MIM seems to have some potential to detect acute ventilation disorders in newborn piglets. The location of the measurement coil related to the animal's position plays a critical role in this process. In addition to an early detection of acute pulmonary problems, potential information pointing to a therapeutic intervention, for example, inhalations or medical respiratory analepsis, may be conceivable with MIM in the future. IMPACT: MIM seems to be a method in which noncontact ventilation disorders of premature and mature infants can be detected. This study is an extension of the experimental setup to obtain preliminary evidence for detection of respiratory activity in neonatal piglets. For the first time, MIM is used to register acute ventilation problems of neonates. The possibility of an early detection of acute ventilation problems via MIM may provide an opportunity to receive patient-side information for therapeutical interventions like inhalations or medical respiratory analepsis.

Estimation of the respiratory rate from ballistocardiograms using the Hilbert transform.

BACKGROUND: Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it. METHODS: In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation. RESULTS: By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved. CONCLUSION: The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.

Non-Contact Measurement of Heart Rate Variability in Frail Geriatric Patients: Response to Early Geriatric Rehabilitation and Comparison with Healthy Old Community-Dwelling Individuals - A Pilot Study.

INTRODUCTION: Frailty is a central geriatric syndrome characterized by a state of increased physiological vulnerability. As the key components of frailty are difficult to capture in their entirety, easily measurable and reliable surrogate parameters are desirable. Since frailty influences heart rate variability (HRV), HRV may be such a surrogate parameter. HRV is typically acquired by an ECG, which, however, may not be tolerated by all patients; in some, it may even trigger delirium. Therefore, we sought to measure HRV in a non-contact and unobtrusive way through photoplethysmography imaging (PPGI). Using our previously presented HRV estimation algorithm for PPGI, we investigated whether PPGI could reveal (1) HRV differences between frail and non-frail individuals and (2) the influences of early geriatric rehabilitation on HRV. METHODS: The study involved 10 frail geriatric inpatients undergoing early geriatric rehabilitation and 10 healthy community-dwelling older adults. All participants underwent a comprehensive geriatric assessment. HRV measurements using a PPGI system and a reference ECG were made at the beginning and the end of the rehabilitation. HRV in terms of LF/HF ratio was analysed for both intra-individual changes during the geriatric rehabilitation and differences between frail geriatric patients and healthy community-dwelling individuals. RESULTS: Across all geriatric patients, the median LF/HF ratio obtained with PPGI was found to be reduced by 0.178 (24.8%) during early geriatric rehabilitation. The assessment at the end of the rehabilitation revealed a simultaneous improvement of the functional state. Moreover, frail geriatric patients had a higher LF/HF ratio than their community-dwelling counterparts. Both observations in PPGI-based HRV were confirmed by the reference. The capability of PPGI to track intra-individual HRV changes was also analysed; a Spearman correlation of ρ = 1.0 between PPGI-based HRV and reference was achieved for 58.8% of the participants. CONCLUSION: Early geriatric rehabilitation improves the functional state, which is associated with an increased HRV. PPGI is capable of detecting HRV changes/trends in that age group. While the tracking of intra-individual HRV changes is also possible, its reliability needs improvement. Nevertheless, the capabilities demonstrated in our study and the non-contact measurement principle of PPGI emphasize its potential for application in geriatric medicine.

A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom.

Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient's skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate's skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia.

Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20-150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

Spatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermograph.

BACKGROUND: Only a small fraction of the information available is generally used in the majority of camera-based sensing approaches for vital sign monitoring. Dedicated skin pixels, for example, fall into this category while other regions are often disregarded early in the processing chain. METHODS: We look at a simple processing chain for imaging where a video stream is converted to several other streams to investigate whether other image regions should also be considered. These streams are generated by mapping spatio-temporal and -spectral features of video segments and, thus, compressing the information contained in several seconds of video and encoding these in a new image. Two typical scenarios are provided as examples to study the applicability of these maps: face videos in a laboratory setting and measurements of a baby in the neonatal intensive care unit. Each measurement consists of the synchronous recording of photoplethysmography imaging (PPGI) and infrared thermography (IRT). We report the results of a visual inspection of those maps, evaluate the root mean square (RMS) contrast of foreground and background regions, and use histogram intersections as a tool for similarity measurements. RESULTS: The maps allow us to distinguish visually between pulsatile foreground objects and an image background, which is found to be a noisy pattern. Distortions in the maps could be localized and the origin could be discovered. The IRT highlights subject contours for the heart frequency band, while silhouettes show strong signals in PPGI. Reflections and shadows were found to be sources of signals and distortions. We can testify advantages for the use of near-infrared light for PPGI. Furthermore, a difference in RMS contrast for pulsatile and non-pulsatile regions could be demonstrated. Histogram intersections allowed us to differentiate between the background and foreground. CONCLUSIONS: We introduced new maps for the two sensing modalities and presented an overview for three different wavelength ranges. The maps can be used as a tool for visualizing aspects of the dynamic information hidden in video streams without automation. We propose focusing on an indirect method to detect pulsatile regions by using the noisy background pattern characteristic, for example, based on the histogram approach introduced.

A Way of Bionic Control Based on EI, EMG, and FMG Signals.

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.

Determination of the Geometric Parameters of Electrode Systems for Electrical Impedance Myography: A Preliminary Study.

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers' MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

Individualized Positive End-expiratory Pressure and Regional Gas Exchange in Porcine Lung Injury.

BACKGROUND: In acute respiratory failure elevated intraabdominal pressure aggravates lung collapse, tidal recruitment, and ventilation inhomogeneity. Low positive end-expiratory pressure (PEEP) may promote lung collapse and intrapulmonary shunting, whereas high PEEP may increase dead space by inspiratory overdistension. The authors hypothesized that an electrical impedance tomography-guided PEEP approach minimizing tidal recruitment improves regional ventilation and perfusion matching when compared to a table-based low PEEP/no recruitment and an oxygenation-guided high PEEP/full recruitment strategy in a hybrid model of lung injury and elevated intraabdominal pressure. METHODS: In 15 pigs with oleic acid-induced lung injury intraabdominal pressure was increased by intraabdominal saline infusion. PEEP was set in randomized order: (1) guided by a PEEP/inspired oxygen fraction table, without recruitment maneuver; (2) minimizing tidal recruitment guided by electrical impedance tomography after a recruitment maneuver; and (3) maximizing oxygenation after a recruitment maneuver. Single photon emission computed tomography was used to analyze regional ventilation, perfusion, and aeration. Primary outcome measures were differences in PEEP levels and regional ventilation/perfusion matching. RESULTS: Resulting PEEP levels were different (mean ± SD) with (1) table PEEP: 11 ± 3 cm H2O; (2) minimal tidal recruitment PEEP: 22 ± 3 cm H2O; and (3) maximal oxygenation PEEP: 25 ± 4 cm H2O; P < 0.001. Table PEEP without recruitment maneuver caused highest lung collapse (28 ± 11% vs. 5 ± 5% vs. 4 ± 4%; P < 0.001), shunt perfusion (3.2 ± 0.8 l/min vs. 1.0 ± 0.8 l/min vs. 0.7 ± 0.6 l/min; P < 0.001) and dead space ventilation (2.9 ± 1.0 l/min vs. 1.5 ± 0.7 l/min vs. 1.7 ± 0.8 l/min; P < 0.001). Although resulting in different PEEP levels, minimal tidal recruitment and maximal oxygenation PEEP, both following a recruitment maneuver, had similar effects on regional ventilation/perfusion matching. CONCLUSIONS: When compared to table PEEP without a recruitment maneuver, both minimal tidal recruitment PEEP and maximal oxygenation PEEP following a recruitment maneuver decreased shunting and dead space ventilation, and the effects of minimal tidal recruitment PEEP and maximal oxygenation PEEP were comparable.

The dawn of physiological closed-loop ventilation-a review.

The level of automation in mechanical ventilation has been steadily increasing over the last few decades. There has recently been renewed interest in physiological closed-loop control of ventilation. The development of these systems has followed a similar path to that of manual clinical ventilation, starting with ensuring optimal gas exchange and shifting to the prevention of ventilator-induced lung injury. Systems currently aim to encompass both aspects, and early commercial systems are appearing. These developments remain unknown to many clinicians and, hence, limit their adoption into the clinical environment. This review shows the evolution of the physiological closed-loop control of mechanical ventilation.

Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary 868MHz communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the 868MHz is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: 99.6±0.8%, Root-Mean-Square Deviation (RMSE) of mean stride time: 5ms, RMSE of percentage stance time: 2.3%). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.