Notes

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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. 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. S3I-201 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. It was not possible to reduce the number of strides for the MaxL or TREND. However, for the RR, DET, AVG, and EntD, it was possible to reduce the number of strides by 60% when analyzed together. The minimum sampling frequency required to extract all quantifiers simultaneously was 100 Hz. This potential reduction in the number of strides is appropriate for evaluating fast gait events, with short temporal localization in the RP, by applying the sliding window method to the recurrence plot. Many diagnostic and some therapeutic ophthalmic devices require a reliable complementing method to track the direction of gaze or just to validate fixation of the eye on a presented target. This would allow acquisition of artefact-free robust images of the fovea and the surrounding macula. So far, there have been only few attempts to provide fast and dependable fixation information to an optical imaging system in real time, to guide image acquisition. The author's lab has developed several instruments that detect the location of the fovea using retinal birefringence scanning (RBS), proven to be very effective. Here, an RBS-based fixation detection subsystem is proposed, designed to operate conjointly with a number of ophthalmic imaging technologies. Combining RBS with such technologies is not trivial, because RBS uses polarized light and polarization-sensitive optics, while most other modalities don't. The polarization optics was optimized by means of enhanced computer modeling. Both the electronic hardware and the software were designed for fast and reliable performance. Because many retinal imaging systems are used in pediatric settings, extensive audio-visual circuitry was employed for efficient attention/fixation attraction. The optomechanics has been optimized for robust data acquisition. This computer-aided conjoint system employs true anatomical information from the back of the eye and needs no calibration. The prototype instrument uses a decision-making logic based on four frequencies generated during scanning. The results reveal the applicability of RBS as an adjunct fixation monitoring modality, showing promise to remove the limitation imposed by eye movements upon advanced ophthalmic imaging technologies. Epilepsy involves brain abnormalities that may cause sudden seizures or other uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality of life, and its detection heavily relies on tedious and time-consuming manual curation by experienced clinicians, based on EEG signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. A new patient can only be monitored effectively after several episodes of epileptic seizures. This study investigates the patient-independent detection of seizure events using the open dataset CHB-MIT Scalp EEG. First, a novel feature extraction algorithm called MinMaxHist is proposed to measure the topological patterns of the EEG signals. Following this, MinMaxHist and several other feature extraction algorithms are applied to parameterize the EEG signals. Next, a comprehensive series of feature screening and classification optimization experiments are conducted, and finally, an optimized EEG-based seizure detection model is presented that can achieve overall values for accuracy, sensitivity, specificity, Matthews correlation coefficient, and Kappa of 0.8627, 0.8032, 0.9222, 0.7504 and 0.7254, respectively, with only 30 features. The classification accuracy of the method with MinMaxHist features was 0.0464 higher than that without MinMaxHist features. Compared with existing methods, the proposed algorithm achieved higher accuracy and sensitivity, as shown in the experimental results. Segmentation of tumors from hybrid PET/MRI scans plays an essential role in accurate diagnosis and treatment planning. However, when treating tumors, several challenges, notably heterogeneity and the problem of leaking into surrounding tissues with similar high uptake, have to be considered. To address these issues, we propose an automated method for accurate delineation of tumors in hybrid PET/MRI scans. The method is mainly based on creating intermediate images. In fact, an automatic detection technique that determines a preliminary Interesting Uptake Region (IUR) is firstly performed. To overcome the leakage problem, a separation technique is adopted to generate the final IUR. Then, smart seeds are provided for the Graph Cut (GC) technique to obtain the tumor map. To create intermediate images that tend to reduce heterogeneity faced on the original images, the tumor map gradient is combined with the gradient image. Lastly, segmentation based on the GCsummax technique is applied to the generated images. The proposed method has been validated on PET phantoms as well as on real-world PET/MRI scans of prostate, liver and pancreatic tumors. Experimental comparison revealed the superiority of the proposed method over state-of-the-art methods. This confirms the crucial role of automatically creating intermediate images in addressing the problem of wrongly estimating arc weights for heterogeneous targets.
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