• Journal of Yeungnam Medical Science
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• v.18 no.1
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• pp.39-50
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• 2001

Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signals can be obtained: R-R interval heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper is efficient for the acquisition of heart rate and respiration signals. This system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.

• Clinical and Experimental Pediatrics
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• v.48 no.8
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• pp.857-864
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• 2005

Purpose : The objectives of this study was to evaluate the correlations between the indices of insulin sensitivity using fasting glucose and insulin level, and the body fat mass measured by bioelectrical impedance analysis(BIA) and dual energy X-ray absorptiometry(DEXA), and to determine the clinical usefulness of insulin sensitivity indices when obese children were followed up. Methods : In this study, 28 simple obese children and adolescents were included. Anthropometric data including body weight, height, obesity degree(OD), body mass index(BMI), and waist-to-hip ratio were collected and then body fat mass was measured by using BIA and DEXA. For metabolic data, 12 hour fasting serum glucose, insulin and lipid profiles were measured and indices for insulin sensitivity(G/I ratio, $log_{insulin}$, HOMA-IR, $log_{HOMA-IR}$, QUICKI) were calculated. Results : BMI had a higher correlation with insulin sensitivity indices than OD(G/I ratio, -0.463 vs -0.209; $log_{insulin}$, 0.417 vs 0.196; HOMA-IR, 0.301 vs 0.238; $log_{HOMA-IR}$, 0.403 vs 0.198; QUICKI, -0.451 vs -0.224). But OD had a higher correlation with body fat mass measured by BIA and DEXA than BMI(BIA, 0.612 vs 0.316; DEXA, 0.667 vs 0.512). The G/I ratio was correlated with body fat mass in BIA(r=-0.420, P<0.05) and DEXA(r=-0.512, P<0.01), percentage of body fat(percentage of fat) in BIA(r=-0.366, P<0.05) and DEXA(r=-0.449, P<0.01). HOMA-IR was only correlated with body fat mass in DEXA(r=0.341, P<0.05). Conclusion : This study revealed that G/I ratios had a statistically significant correlation with anthropometric obesity indices(OD and BMI) and also had a correlation with both body fat mass and percentage of fat. These results suggest that G/I ratios could be used as useful index when obese children and adolescence are followed up.