Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously ...breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics.
A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison.
At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator.
The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.
•Mode-switching moving average reduces current noise and improve torque confidence.•Time period adapts to achieve tradeoff between response time and precision.•Calibration routines take motor current ...noise and speed errors into account.•Adaptive Kalman filter with variable time period facilitates force/torque estimation.
Contact force and torque sensing approaches enable manipulators to cooperate with humans and to interact appropriately with unexpected collisions. In this paper, a mode-switching moving average with variable time period is proposed to reduce the effects of measured motor current noise and thus provide improved confidence in joint output torque estimation. The time period of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real-time. An adaptive Kalman filter that consists of the proposed moving average and the classical Kalman filter is proposed. Calibration routines for the adaptive Kalman filter take the measured motor current noise and errors in the speed data from the individual joints into account. The combination of the proposed adaptive Kalman filter with variable time period and its calibration method facilitates force and torque estimation without force/torque sensors. Contact force/torque sensing and response time assessments from the proposed approach were performed on the Universal Robot 5 manipulator with differing unexpected end effector loads. The combined force and torque sensing method led to a reduction of the estimation errors and response time in comparison with the pioneering method, and the effect is further improved as the payload rises. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) are available and consequently the cost will be reduced dramatically from methods that require load cells.
Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to ...minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3–20), and it reduced the parameter variance metric by 33–37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.
Abstract The significance of biomechanical analyses for forensic time since death estimations has recently been demonstrated. Previous biomechanical analyses successfully discriminated post-mortem ...brain tissue from tissue with a post-mortem interval of at least one day when held at 20 °C. However, the practical utility of such analyses beyond day one at 20 °C was limited. This study investigates the storage, loss, and complex shear modulus of various brain regions in sheep stored at 4 °C in 24-hour intervals over four days post-mortem using rheometry tests. The aim is to identify the critical biomechanical tissue property values to predict post-mortem time and assess the temperature sensitivity of the rheometry method by comparing results to recent findings at 20 °C. Thirty sheep brains were examined, including the frontal lobe, parietal lobe, anterior and posterior deep brain, superior colliculi, pons, medulla, and cerebellum. Rheometry tests were conducted, and receiver operator characteristic analyses were employed to establish cut-off values. At 4 °C storage, all investigated biomechanical properties of the examined brain regions remained stable for at least one day post-mortem. Using cerebellar samples stored at 4 °C, a post-mortem interval of at least two days could be determined with excellent diagnostic ability. Complex shear modulus values below 1435 Pa or storage modulus values below 1313 Pa allowed prediction of two or more days post-mortem. Comparisons between 4 °C and 20 °C revealed brain region-specific results. For instance, the complex shear moduli of the anterior deep brain at 4 °C were significantly higher on all individual testing days when compared to 20 °C. In contrast, the combined medulla and pons samples were similar on each day. Rheometry testing of brain tissue consistently stored at 4 °C since death proved valuable for forensic time since death estimations starting from two days after death.
Increased incidence of traumatic brain injury (TBI) imposes a growing need to understand the pathology of brain trauma. A correlation between the incidence of multiple brain traumas and rates of ...behavioural and cognitive deficiencies has been identified amongst people that experienced multiple TBI events. Mechanically, repetitive TBIs may affect brain tissue in a similar way to cyclic loading. Hence, the potential susceptibility of brain tissue to mechanical fatigue is of interest. Although temporal changes in ovine brain tissue viscoelasticity and biological fatigue of other tissues such as tendons and arteries have been investigated, no methodology currently exists to cyclically load ex vivo brain tissue. A novel rheology-based approach found a consistent, initial stiffening response of the brain tissue before a notable softening when subjected to a subsequential cyclic rotational shear. History dependence of the mechanical properties of brain tissue indicates susceptibility to mechanical fatigue. Results from this investigation increase understanding of the fatigue properties of brain tissue and could be used to strengthen therapy and prevention of TBI, or computational models of repetitive head injuries.
Sensorless contact force estimation methods facilitate the application of the serial manipulators to manufacturing as they enable robots to interact with unexpected collisions at low cost. In this ...paper, an external force estimation approach with no embedded sensors is proposed. The approach combines a Weighted Moving Average (WMA) with variable span, the standard Kalman filter (SKF), and its tuning routines. Improved confidence in the motor output torque is achieved due to the reduction of the measurement noise in the motor current by the WMA. The span of the filter adapts continuously to achieve optimal tradeoff between response time and precision of estimation in real time. With the comprehensive information of uncertainty in motor current noise and measurement errors of individual joints speed, an automatic tuning algorithm of the SKF is presented. Validation of the presented estimation approach in terms of estimation accuracy and response time was conducted on the Universal Robot 5 manipulator with differing end effector loads. It was found that the combined force estimation method leads to a reduction of the root-mean-square error and response time by 55.2% and 20.8% in comparison with the established method. The proposed method can be applied to any robotic manipulators as long as the motor information (current, joint position, and joint velocities) is available. Consequently, the cost of collision recognition could be reduced dramatically.
Fusing data from different medical perspectives inside the operating room (OR) sets the stage for developing intelligent context-aware systems. These systems aim to promote better awareness inside ...the OR by keeping every medical team well informed about the work of other teams and thus mitigate conflicts resulting from different targets. In this research, a descriptive analysis of data collected from anaesthesiology and surgery was performed to investigate the relationships between the intra-abdominal pressure (IAP) and lung mechanics for patients during laparoscopic procedures. Data of nineteen patients who underwent laparoscopic gynaecology were included. Statistical analysis of all subjects showed a strong relationship between the IAP and dynamic lung compliance (r = 0.91). Additionally, the peak airway pressure was also strongly correlated to the IAP in volume-controlled ventilated patients (r = 0.928). Statistical results obtained by this study demonstrate the importance of analysing the relationship between surgical actions and physiological responses. Moreover, these results form the basis for developing medical decision support models, e.g., automatic compensation of IAP effects on lung function.
Convolutional neural networks (CNNs) have become a useful tool for a wide range of applications such as text classification. However, CNNs are not always sufficiently accurate to be useful in certain ...applications. The selection of activation functions within CNN architecture can affect the efficacy of the CNN. However, there is limited research regarding which activation functions are best for CNN text classification. This study tested sixteen activation functions across three text classification datasets and six CNN structures, to determine the effects of activation function on accuracy, iterations to convergence, and Positive Confidence Difference (PCD). PCD is a novel metric introduced to compare how activation functions affected a network’s classification confidence. Tables were presented to compare the performance of the activation functions across the different CNN architectures and datasets. Top performing activation functions across the different tests included the symmetrical multi-state activation function, sigmoid, penalised hyperbolic tangent, and generalised swish. An activation function’s PCD was the most consistent evaluation metric during activation function assessment, implying a close relationship between activation functions and network confidence that has yet to be explored.
Purpose:
The kissing stent (KS) method is low-risk compared with open surgery techniques. It is often used to treat aorto-iliac occlusive disease (AIOD). Deployment of the KS geometry has a high ...technical success rate. However, stent patency reduces in the first 5 years potentially due to deleterious flow behavior. Potentially harmful hemodynamics due to the KS were investigated in vitro.
Methodology:
A compliant phantom of the aorto-iliac bifurcation was manufactured. Two surrogate stent-grafts were deployed into the phantom in the KS configuration to investigate effects of the presence of the stents, including the compliance mismatch they cause, on the hemodynamics proximal and distal to the KS. The investigation used pulsatile flow through a flow circuit to simulate abdominal aortic flow. Particle image velocimetry (PIV) was used to quantify the hemodynamics.
Results:
PIV identified peak proximal and distal velocity in vitro was 0.71 and 1.40m·s−1, respectively, which were within physiological ranges. Throughout systole, flow appeared normal and undisturbed. A lumen wall collapse in the sagittal plane formed during late systole and continued to early diastole proximal to the aorto-iliac bifurcation, distal to the inlet stent position. The wall collapse led to disturbed flow proximal to the stented region in early diastole producing potential recirculation zones and abnormal flow patterns.
Conclusion:
The normal systolic flow behavior indicates the KS configuration is unlikely to cause an inflammatory response of the arterial walls. The collapse has not been previously identified and may potentially cause long-term patency reduction. It requires further investigation.
Clinical Impact
The role of this article is to provide further insight into the haemodynamic behavior through a stented aorto-iliac artery. The results of this investigation will improve the understanding of the effects that using the kissing stent method may have on a patient and help to identify high risk regions that may require more detailed monitoring. This paper also develops the in vitro modelling techniques that will enable further research that cannot be carried out within patients.
Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack ...the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations-such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.
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