A new photoplethysmographic signal analysis algorithm for calculating the ageing index significantly differentiated between healthy subjects and diabetes patients (P < 0.0005).
Observational (n=41)
Does a new SDPPG analysis algorithm effectively estimate arterial stiffness and differentiate between healthy subjects and diabetes patients?
A new SDPPG analysis algorithm effectively estimates arterial stiffness and can significantly differentiate between healthy individuals and those with diabetes.
p-value: p=<0.0005
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 y AGI was found to be 0.359. The difference was found between healthy and diabetes patients groups with significance level of P < 0.0005.
Pilt et al. (Tue,) conducted a observational in Arterial stiffness (n=41). Diabetes vs. Healthy subjects was evaluated on Ageing index (AGI) (p=<0.0005). A new photoplethysmographic signal analysis algorithm for calculating the ageing index significantly differentiated between healthy subjects and diabetes patients (P < 0.0005).