Validity of visceral adiposity estimates from DXA against MRI in Kuwaiti men and women.

OBJECTIVES: The prevalence of obesity and diabetes in the Middle East is among the highest in the world. Valid measures of abdominal adiposity are essential to understanding the metabolic consequences of obesity. Dual-energy X-ray absorptiometry (DXA) is increasingly being utilised to assess body composition in population studies, and has recently been used to estimate visceral adipose tissue (VAT). The aim of this study was to determine the accuracy of DXA-derived VAT in a Middle Eastern population using magnetic resonance imaging (MRI) as the criterion measure. METHOD: VAT was estimated from abdominal DXA measures in 237 adult men (n=130) and women (n=107), aged 18-65 years, participating in the Kuwait Wellbeing Study. These estimates were compared with MRI measures of the corresponding anatomical region. The agreement between methods was assessed using Bland-Altman as well as correlation analysis. RESULTS: Median MRI VAT was 1148.5 cm3 (95% confidence interval: 594.2-1734.6) in men and 711.3 cm3 (95% confidence interval: 395.5-1042.8) in women. DXA estimates of VAT showed high correlations with corresponding MRI measures (r=0.94 (P<0.0001) in men; r=0.93 (P<0.0001) in women). DXA overestimated VAT with a mean bias (95% limits of agreement) of 79.7 cm3 (-767 to 963) in men and 46.8 cm3 (-482 to 866) in women. The imprecision of DXA increased with increasing VAT level in both men and women. CONCLUSION: DXA estimates of VAT are valid for use in Middle Eastern populations, although accuracy decreases with increasing level of visceral adiposity.

The Kuwait-Scotland eHealth Innovation Network (KSeHIN): a sustainable approach to quality improvement in healthcare.

BACKGROUND: The rising prevalence of obesity and diabetes in Kuwait represents a significant challenge for the country's healthcare system. Diabetes care in Scotland has improved by adopting a system of managed clinical networks supported by a national informatics platform. In 2010, a Kuwait-Dundee collaboration was established with a view to transforming diabetes care in Kuwait. This paper describes the significant progress that has been made to date. METHODS: The Kuwait-Scotland eHealth Innovation Network (KSeHIN) is a partnership among health, education, industry and government. KSeHIN aims to deliver a package of clinical service development, education (including a formal postgraduate programme and continuing professional development) and research underpinned by a comprehensive informatics system. RESULTS: The informatics system includes a disease registry for children and adults with diabetes. At the patient level, the system provides an overview of clinical and operational data. At the population level, users view key performance indicators based on national standards of diabetes care established by KSeHIN. The national childhood registry (CODeR) accumulates approximately 300 children a year. The adult registry (KHN), implemented in four primary healthcare centres in 2013, has approximately 4000 registered patients, most of whom are not yet meeting national clinical targets. A credit-bearing postgraduate educational programme provides module-based teaching and workplace-based projects. In addition, a new clinical skills centre provides simulator-based training. Over 150 masters students from throughout Kuwait are enrolled and over 400 work-based projects have been completed to date. CONCLUSION: KSeHIN represents a successful collaboration between multiple stakeholders working across traditional boundaries. It is targeting patient outcomes, system performance and professional development to provide a sustainable transformation in the quality of diabetes healthcare for the growing population of Kuwaitis with diabetes in Kuwait.

Cardiovascular disease risk factors in the South Asian population living in Kuwait: a cross-sectional study.

BACKGROUND: High rates of diabetes and cardiovascular disease have been reported in South Asian immigrants in many countries. However, the prevalence and characteristics of cardiovascular disease risk factors among a South Asian population living in Kuwait have not yet been investigated. This study was therefore designed to estimate the prevalence of cardiovascular disease risk factors and determine whether they are independently associated with diabetes in such a population. METHODS: A population-based cross-sectional study was conducted on 1094 South Asians (781 men and 313 women), mainly Indian and Pakistani (≥ 18 years of age), of whom 75.1% were Indians. Interviews were carried out, during which socio-demographic and anthropometric data were collected, followed by a physical examination and collection of fasting blood samples for laboratory investigations. Diabetes was defined by fasting plasma glucose ≥ 7 mmol/l, or being on treatment, and/or self-reported previously diagnosed Type 2 diabetes. RESULTS: The prevalence of diabetes was 21.1%, with 3.4% of that percentage of people being newly diagnosed. Using BMI measurements, 24.0% of those who participated in the study were obese and 46.1% were overweight. Dyslipidaemia was found in 77.6% and hypertension in 44.8%. Advancing age (≥ 40 years), male gender, high LDL, high total cholesterol, hypertension and positive family history of diabetes were significantly associated with increased risk of diabetes. CONCLUSION: Our study shows that the prevalence of cardiovascular disease risk factors in South Asian expatriates in Kuwait exceeds prevalence rates reported in their homeland and other countries. This may suggest the added stress of environmental factors on the development of cardiovascular disease risk factors in such populations. Specialized prevention programmes targeting such high-risk ethnic populations are paramount and need to be implemented.

EOG and EMG: two important switches in automatic sleep stage classification.

Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%

Itakura Distance: A Useful Similarity Measure between EEG and EOG Signals in Computer-aided Classification of Sleep Stages.

Sleep is a natural periodic state of rest for the body, in which the eyes usually close and consciousness is completely or partially lost. Consequently, there is a decrease in bodily movements and responsiveness to external stimuli. Slow wave sleep is of immense interest as it is the most restorative sleep stage during which the body recovers from weariness. During this sleep stage, electroencephalographic (EEG) and electro-oculographic (EOG) signals interfere with each other and they share a temporal similarity. In this investigation we used the EEG and EOG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by certified sleep specialists based on RK rules. In this pilot study, we performed spectral estimation of EEG signals by Autoregressive (AR) modeling, and then used Itakura Distance to measure the degree of similarity between EEG and EOG signals. We finally calculated the statistics of the results and displayed them in an easy to visualize fashion to observe tendencies for each sleep stage. We found that Itakura Distance is the smallest for sleep stages 3 and 4. We intend to deploy this feature as an important element in automatic classification of sleep stages.

The use of R-wave morphology in the detection of sleep-disordered breathing using the electrocardiogram--a comparison between leads.

Power spectral analysis of time series derived from the R-wave morphology of the ECG was employed to identify a suitable lead configuration for the detection of sleep-disordered breathing (SDB) using the electrocardiogram (ECG). 16 subjects (46 +/- 9.2 yrs, 8 males), who did not report problems during sleep, and 13 subjects previously diagnosed with SDB (49 +/- 8.8 yrs, 7 males) underwent an overnight sleep study at an accredited sleep center. Power values derived from the spectra of the R-peaks envelope were tested for their sensitivity and specificity in discriminating between epochs containing normal breathing from epochs containing SDB. Of the three tested lead configurations using two parameters NB1 and NB2 derived from the power spectrum, lead I produced the best results with a sensitivity of 92.8% and a specificity of 88.0% for the case of parameter NB1 and a sensitivity of 85.7% and a specificity of 76.0% for the case of parameter NB2.

EEG feature extraction for classification of sleep stages.

Automated sleep staging based on EEG signal analysis provides an important quantitative tool to assist neurologists and sleep specialists in the diagnosis and monitoring of sleep disorders as well as evaluation of treatment efficacy. A complete visual inspection of the EEG recordings acquired during nocturnal polysomnography is time consuming, expensive, and often subjective. Therefore, feature extraction is implemented as an essential preprocessing step to achieve significant data reduction and to determine informative measures for automatic sleep staging. However, the analysis of the EEG signal and extraction of sensitive measures from it has been a challenging task due to the complexity and variability of this signal. We present three different schemes to extract features from the EEG signal: relative spectral band energy, harmonic parameters, and Itakura distance. Spectral estimation is performed by using autoregressive (AR) modeling. We then compare the performance of these schemes with the view to select an optimal set of features for specific, sensitive, and accurate neuro-fuzzy classification of sleep stages.

Kalman filter modeling of cerebral blood flow autoregulation.

A parameter estimation scheme for dynamic systems is employed to simultaneously estimate the states and parameters of the model of human cerebral blood flow velocity as a function of mean arterial blood pressure. The estimation results show 20-40% reduction in the output mean square error compared to that of the one obtained from the computer model addressed in [1]. The estimation scheme estimates the parameters and states of the system, as well as the level of the observed and process noise variances. This approach is more extensive than the one that was applied to the same system in the previous work [2], in which only the Kalman filter was applied and the system was restricted to some specific constraints.

Dynamic regulation of heart rate during acute hypotension: new insight into baroreflex function.

UNLABELLED: To examine the dynamic properties of baroreflex function, we measured beat-to-beat changes in arterial blood pressure (ABP) and heart rate (HR) during acute hypotension induced by thigh cuff deflation in 10 healthy subjects under supine resting conditions and during progressive lower body negative pressure (LBNP). The quantitative, temporal relationship between ABP and HR was fitted by a second-order autoregressive (AR) model. The frequency response was evaluated by transfer function analysis. RESULTS: HR changes during acute hypotension appear to be controlled by an ABP error signal between baseline and induced hypotension. The quantitative relationship between changes in ABP and HR is characterized by a second-order AR model with a pure time delay of 0.75 s containing low-pass filter properties. During LBNP, the change in HR/change in ABP during induced hypotension significantly decreased, as did the numerator coefficients of the AR model and transfer function gain. CONCLUSIONS: 1) Beat-to-beat HR responses to dynamic changes in ABP may be controlled by an error signal rather than directional changes in pressure, suggesting a "set point" mechanism in short-term ABP control. 2) The quantitative relationship between dynamic changes in ABP and HR can be described by a second-order AR model with a pure time delay. 3) The ability of the baroreflex to evoke a HR response to transient changes in pressure was reduced during LBNP, which was due primarily to a reduction of the static gain of the baroreflex.