Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 5 Articles
Background: Body electrical loss analysis (BELA) is a new non-invasive way to assess\r\nvisceral fat depot size through the use of electromagnetism. BELA has worked well in\r\nphantom measurements, but the technology is not yet fully validated.\r\nMethods: Ten volunteers (5 men and 5 women, age: 22-60 y, BMI: 21-30 kg/m2,\r\nwaist circumference: 73-108 cm) were measured with the BELA instrument and with\r\ncross-sectional magnetic resonance imaging (MRI) at the navel level, navel cm\r\nand navel -5 cm. The BELA signal was compared with visceral and subcutaneous fat\r\nareas calculated from the MR images.\r\nResults: The BELA signal did not correlate with subcutaneous fat area at any level,\r\nbut correlated significantly with visceral fat area at the navel level and navel cm.\r\nThe correlation was best at level of navel cm (R2 = 0.74, P < 0.005, SEE = 29.7\r\ncm2, LOOCV = 40.1 cm2), where SEE is the standard error of the estimate and LOOCV\r\nis the root mean squared error of leave-one-out style cross-validation. The average\r\nestimate of repeatability of the BELA signal observed through the study was �±9.6 %.\r\nOne of the volunteers had an exceptionally large amount of visceral fat, which was\r\nunderestimated by BELA.\r\nConclusions: The correlation of the BELA signal with the visceral but not with the\r\nsubcutaneous fat area as measured by MRI is promising. The lack of correlation with\r\nthe subcutaneous fat suggests that subcutaneous fat has a minor influence to the\r\nBELA signal. Further research will show if it is possible to develop a reliable low-cost\r\nmethod for the assessment of visceral fat either using BELA only or combining it, for\r\nexample, with bioelectrical impedance measurement. The combination of these\r\nmeasurements may help assessing visceral fat in a large scale of body composition.\r\nBefore large-scale clinical testing and ROC analysis, the initial BELA instrumentation\r\nrequires improvements. The accuracy of the present equipment is not sufficient for\r\nsuch new technology....
Background: Monitoring of vital parameters is an important topic in neonatal daily\r\ncare. Progress in computational intelligence and medical sensors has facilitated the\r\ndevelopment of smart bedside monitors that can integrate multiple parameters into\r\na single monitoring system. This paper describes non-contact monitoring of neonatal\r\nvital signals based on infrared thermography as a new biomedical engineering\r\napplication. One signal of clinical interest is the spontaneous respiration rate of the\r\nneonate. It will be shown that the respiration rate of neonates can be monitored\r\nbased on analysis of the anterior naris (nostrils) temperature profile associated with\r\nthe inspiration and expiration phases successively.\r\nObjective: The aim of this study is to develop and investigate a new non-contact\r\nrespiration monitoring modality for neonatal intensive care unit (NICU) using infrared\r\nthermography imaging. This development includes subsequent image processing\r\n(region of interest (ROI) detection) and optimization. Moreover, it includes further\r\noptimization of this non-contact respiration monitoring to be considered as\r\nphysiological measurement inside NICU wards.\r\nResults: Continuous wavelet transformation based on Debauches wavelet function\r\nwas applied to detect the breathing signal within an image stream. Respiration was\r\nsuccessfully monitored based on a 0.3�°C to 0.5�°C temperature difference between\r\nthe inspiration and expiration phases.\r\nConclusions: Although this method has been applied to adults before, this is the\r\nfirst time it was used in a newborn infant population inside the neonatal intensive\r\ncare unit (NICU). The promising results suggest to include this technology into\r\nadvanced NICU monitors....
Background: Newborn mammals suffering from moderate hypoxia during or after\r\nbirth are able to compensate a transitory lack of oxygen by adapting their vital\r\nfunctions. Exposure to hypoxia leads to an increase in the sympathetic tone causing\r\ncardio-respiratory response, peripheral vasoconstriction and vasodilatation in\r\nprivileged organs like the heart and brain. However, there is only limited information\r\navailable about the time and intensity changes of the underlying complex processes\r\ncontrolled by the autonomic nervous system.\r\nMethods: In this study an animal model involving seven piglets was used to\r\nexamine an induced state of circulatory redistribution caused by moderate oxygen\r\ndeficit. In addition to the main focus on the complex dynamics occurring during\r\nsustained normocapnic hypoxia, the development of autonomic regulation after\r\ninduced reoxygenation had been analysed. For this purpose, we first introduced a\r\nnew algorithm to prove stationary conditions in short-term time series. Then we\r\ninvestigated a multitude of indices from heart rate and blood pressure variability and\r\nfrom bivariate interactions, also analysing respiration signals, to quantify the\r\ncomplexity of vegetative oscillations influenced by hypoxia.\r\nResults: The results demonstrated that normocapnic hypoxia causes an initial\r\nincrease in cardiovascular complexity and variability, which decreases during\r\nmoderate hypoxia lasting one hour (p < 0.004). After reoxygenation, cardiovascular\r\ncomplexity parameters returned to pre-hypoxic values (p < 0.003), however not\r\nrespiratory-related complexity parameters.\r\nConclusions: In conclusion, indices from linear and nonlinear dynamics reflect\r\nconsiderable temporal changes of complexity in autonomous cardio-respiratory\r\nregulation due to normocapnic hypoxia shortly after birth. These findings might be\r\nsuitable for non-invasive clinical monitoring of hypoxia-induced changes of\r\nautonomic regulation in newborn humans....
Background: A proper sleep system can affect the spine support in neutral position.\r\nMost of the previous studies in scientific literature have focused on the effects of\r\ncustomary mattresses on the spinal alignment. To keep the spine in optimal alignment,\r\none can use sleep surfaces with different zonal elasticity, the so called custom-made\r\narrangements. The required stiffness of a sleep surface for each individual can be obtained\r\nby changing this arrangement applying the experimental method and modeling.\r\nMethods: In experimental part, the coordinate positions of the markers mounted on\r\nthe spinous processes of the vertebrae of 25 male volunteers were registered in\r\nfrontal plane through the optical tracking method and so the spinal alignment was\r\nobtained in lateral sleep position on soft and firm surfaces and on the best custommade\r\narrangement. Thereupon the p-P8 angles were extracted from these\r\nalignments and then were compared with each other. In modeling part the\r\nanthropometric data of four different types of volunteers were used. And then the\r\nmodels built in BRG.LifeMOD (ver. 2007, Biomechanics Research Group, Inc., USA)\r\nbased on these data and in accordance with the experimental tests, were analyzed.\r\nResults: The one way ANOVA statistical model and the post hoc tests showed a\r\nsignificant difference in the p-P8 angles between soft & custom-made and soft & firm\r\nmattresses at the p = 0.001 level and between firm & soft mattresses at the p = 0.05\r\nlevel. In modeling part, the required stiffness of the sleep surface for four weightdimensional\r\ngroups was acquired quantitatively.\r\nConclusions: The mattress with a custom-made arrangement is a more appropriate\r\nchoice for heavier men with pronounced body contour. After data fitting, it was\r\nobserved that the variations of spinal alignment obtained from both methods have\r\nthe same trend. Observing the amount of required stiffness obtained for the sleep\r\nsurface, can have a significant effect on keeping the spine healthy....
Background: Statistical learning (SL) techniques can address non-linear relationships\r\nand small datasets but do not provide an output that has an epidemiologic\r\ninterpretation.\r\nMethods: A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer\r\npatients was used to evaluate an approach for using SL methods as a preprocessing\r\nstep for survival analysis. A stochastic method of training a probabilistic neural\r\nnetwork (PNN) was used with differential evolution (DE) optimization. Survival scores\r\nwere derived stochastically by combining CVs with the PNN. Patients (n = 151) were\r\ndichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome\r\ngroups. These PNN derived scores were used with logistic regression (LR) modeling\r\nto predict favorable survival outcome and were integrated into the survival analysis\r\n(i.e. Kaplan-Meier analysis and Cox regression). The hybrid modeling was compared\r\nwith the respective modeling using raw CVs. The area under the receiver operating\r\ncharacteristic curve (Az) was used to compare model predictive capability. Odds\r\nratios (ORs) and hazard ratios (HRs) were used to compare disease associations with\r\n95% confidence intervals (CIs).\r\nResults: The LR model with the best predictive capability gave Az = 0.703. While\r\ncontrolling for gender and tumor grade, the OR = 0.63 (CI: 0.43, 0.91) per standard\r\ndeviation (SD) increase in age indicates increasing age confers unfavorable outcome.\r\nThe hybrid LR model gave Az = 0.778 by combining age and tumor grade with the\r\nPNN and controlling for gender. The PNN score and age translate inversely with\r\nrespect to risk. The OR = 0.27 (CI: 0.14, 0.53) per SD increase in PNN score indicates\r\nthose patients with decreased score confer unfavorable outcome. The tumor grade\r\nadjusted hazard for patients above the median age compared with those below the\r\nmedian was HR = 1.78 (CI: 1.06, 3.02), whereas the hazard for those patients below\r\nthe median PNN score compared to those above the median was HR = 4.0 (CI: 2.13,\r\n7.14).\r\nConclusion: We have provided preliminary evidence showing that the SL\r\npreprocessing may provide benefits in comparison with accepted approaches. The\r\nwork will require further evaluation with varying datasets to confirm these findings....
Loading....