Land Assembly for Housing Developments

The ability to identify premature arterial stiffening is of considerable value in the prevention of cardiovascular diseases. The “ageing index” (AGI), which is calculated from the second derivative photoplethysmographic (SDPPG) waveform, has been used as one method for arterial stiffness estimation and the evaluation of cardiovascular ageing. In this study, the new SDPPG analysis algorithm is proposed with optimal filtering and signal normalization in time. The filter parameters were optimized in order to achieve the minimal standard deviation of AGI, which gives more effective differentiation between the levels of arterial stiffness. As a result, the optimal low-pass filter edge frequency of 6 Hz and transitionband of 1 Hz were found, which facilitates AGI calculation with a standard deviation of 0.06. The study was carried out on 21 healthy subjects and 20 diabetes patients. The linear relationship (r=0.91) between each subject’s age and AGI was found, and a linear model with regression line was constructed. For diabetes patients, the mean AGI value difference from the proposed model yAGI was found to be 0.359. The difference was found between healthy and diabetes patients groups with significance level of P<0.0005

New Photoplethysmographic Signal Analysis Algorithm for Arterial Stiffness Estimation

The ability to identify premature arterial stiffening is of considerable value in the prevention of cardiovascular diseases. The "ageing index" (AGI), which is calculated from the second derivative photoplethysmographic (SDPPG) waveform, has been used as one method for arterial stiffness estimation and the evaluation of cardiovascular ageing. In this study, the new SDPPG analysis algorithm is proposed with optimal filtering and signal normalization in time. The filter parameters were optimized in order to achieve the minimal standard deviation of AGI, which gives more effective differentiation between the levels of arterial stiffness. As a result, the optimal low-pass filter edge frequency of 6 Hz and transitionband of 1 Hz were found, which facilitates AGI calculation with a standard deviation of 0.06. The study was carried out on 21 healthy subjects and 20 diabetes patients. The linear relationship (r = 0.91) between each subjects age and AGI was found, and a linear model with regression line was constructed. For diabetes patients, the mean AGI value difference from the proposed model y(AGI) was found to be 0.359. The difference was found between healthy and diabetes patients groups with significance level of P andlt; 0.0005.Funding Agencies|Estonian Science Foundation|7506|Estonian Targeted Financing Project|SF0140027s07|European Union through the European Regional Development Fund||</p