Delaneyfinch3081

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs. Published by Elsevier Ltd.Acute kidney injury (AKI) is a major complication following cardiac surgery requiring cardiopulmonary bypass (CPB). It is likely that poor renal perfusion contributes to the occurrence of AKI, via renal hypoxia, so it is imperative to maintain optimal renal perfusion during CPB. We have developed a straightforward cardiovascular perfusion model with parameter values calibrated against experimental and/or clinical data from several independent studies of CPB in humans and animals. Following model development and calibration, we performed a one-at-a-time parametric study to investigate the response of renal perfusion to several variables during CPB, namely pump flow (denoted CO for 'cardiac output'), renal vascular resistance, and non-renal vascular resistance. From the parametric study, we have found that all three parameters had a similarly strong influence on renal perfusion. We simulated three potential strategies for maintaining optimum renal perfusion during CPB and tested their effectiveness. The strategies were (1) increasing the pump flow; (2) administrating noradrenaline (vasopressor); and (3) administrating fenoldopam (renal vasodilator). Simulations have revealed that administration of fenoldopam is likely to be the most effective of the three strategies. Other findings from our simulations are that increasing pump flow is less effective when central venous pressure is elevated. Further, renal autoregulation is likely inoperative during CPB, as evidenced by an unchanging renal vascular resistance with increasing CO and blood pressure. The cardiac-renal perfusion model developed in this study can be linked with other kidney models to simulate the changes in renal oxygenation during CPB. Different frequency components of the lung, which have not been fully considered in traditional computer-aided detection systems for pulmonary nodules, can cause heterogeneous energy distribution. Hence, spectral analysis, which is an important time-frequency representation tool, is utilized to characterize the frequency-dependent energy responses of nodules. In this study, a novel spectral-analysis-based method for nodule candidate detection is presented. The optimal fractional S-transform is applied to transform raw computed tomography images from the spatial to time-frequency domain. Next, a time-frequency cube is decomposed using spectral decomposition to a frequency-dependent energy slice. Subsequently, an energy distribution is obtained by the Teager-Kaiser energy (TKE) to characterize the nodules. Finally, nodule candidates are detected using rule-based and threshold algorithms in the TKE image. The proposed method is validated on a clinical CT data set from Sichuan Provincial People's Hospital. selleck The signal-to-clutter ratio (SCR) increases by 35.5% with respect to raw CT slices. Furthermore, the proposed method exhibits a sensitivity of 97.87%, with only 6.8 false positives per slice. The total number of nodule candidates has an average reduction of 50%. The results indicate that the time-frequency features can effectively characterize solid nodules. Moreover, the proposed method demonstrates accurate detection and can reduce the number of false positive efficiently. Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences. This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term-preterm diagnosis. The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods. In this study, the influence of the sampling frequency and number of strides on recurrence quantifiers extracted from gait data was investigated in order to provide baseline values and preserve the system's non-linear dynamical characteristics expressed by these recurrence quantifiers. Recurrence quantifiers were extracted from a recurrence plot (RP), which required the reconstruction of a high-dimensional state space capable of reproducing the dynamical characteristics of the analyzed system. In this study, the following quantifiers were extracted rate of recurrence (RR), determinism (DET), average diagonal lines length (AVG), maximum diagonal lines length (MaxL), Shannon entropy (EntD), and measure of trend (TREND). Data collected during treadmill walking were statistically analyzed to compare the distribution characteristics (mean, median, and standard deviation) and the quantifiers' correlation with those obtained from a control time series with an acquisition time corresponding to 150 strides and a 100-Hz sampling frequency, which are common values used in gait studies.