To the Editor

Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

BACKGROUND

For the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristics of the incident and reflected waveforms of the photoplethysmogram or because of the lack of expressive power of the regression model, a reliable arterial stiffness assessment technique based on a single photoplethysmogram has not yet been proposed.

OBJECTIVE

The purpose of this study is to discover highly correlated features from the incident and reflected waves decomposed from a photoplethysmogram waveform and to develop an artificial neural network-based regression model for the assessment of vascular aging using newly derived features.

METHODS

We obtained photoplethysmograms from 757 participants. All recorded photoplethysmograms were segmented for each beat, and each waveform was decomposed into incident and reflected waves by the Gaussian mixture model. The 26 basic features and 52 combined features were defined from the morphological characteristics of the incident and reflected waves. The regression model of the artificial neural network was developed using the defined features.

RESULTS

In correlation analysis, the features from the amplitude of the reflected wave and the skewness of the photoplethysmogram showed a relatively strong correlation with the participant's real age. In the estimation of real age, the artificial neural network model showed 10.0 years of root mean square error. Its estimated age and real age had a strong correlation of 0.63 (P<.001).

CONCLUSIONS

This study proved that the features defined from the reflected wave and skewness of the photoplethysmogram are useful to assess vascular aging. Moreover, the regression model of artificial neural network using these features shows the feasibility for the estimation of vascular aging.

Relationship between vasomotor symptoms and metabolic syndrome in Chinese middle-aged women

PURPOSE

This study aimed to find the associations between vasomotor symptoms (VMS) and metabolic syndrome (MetS) in Chinese middle-aged women in a cross-sectional study.

METHODS

A total of 675 participants were recruited from an urban Chinese community. MetS was defined by the 2009 criteria of the Joint Interim Statement. VMS including hot flashes and sweats, blood pressure, weight, height, waist circumference (WC), serum glucose, triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), estradiol, and follicle-stimulating hormone (FSH) were collected.

RESULTS

The presence of hot flashes was independently associated with the risk of MetS after adjusting for age, menopausal status, FSH_log, estradiol_log, and physical activity (odds ratio: 1.98, 95% confidence interval: 1.21-3.24, p = 0.006). Both hot flashes and sweats were also independently associated with WC (for hot flashes, p = 0.016; and for sweats, p = 0.007) and triglycerides (for hot flashes, p = 0.041; and for sweats, p = 0.014) significantly. However, VMS were not significantly associated with blood pressure, glucose, HDL, and LDL.

CONCLUSION

Women with hot flashes had a higher risk of MetS. Both hot flashes and sweats were related to a higher amount of central fat indicated by WC and higher triglycerides, but were not related to blood pressure, glucose, and HDL in Chinese women.