Mathematical model presenting to assess variations in heart rate of ‎different age groups

Recently, older people's cardiovascular systems have been affected by aging-related changes. An electrocardiogram (ECG) provides information about cardiac health. Analyzing ECG signals can help doctors and researchers diagnose many deaths. ‎Besides direct ECG analysis, some measurements can be extracted from the ECG signals, and ‎one of the most important measurements is heart rate variability (HRV). Research and clinical domains can benefit from HRV measurement and analysis as a potential noninvasive tool for assessing autonomic nervous system activity. The HRV describes the variation between an ECG signal's RR intervals over time and the change in that interval over time. An individual's heart rate (HR) is a non-stationary signal, and its variation can indicate a medical condition or impending cardiac disease. Many factors, such as stress, gender, disease, and age, influence HRV.

The data for this study is taken from a standard database, the Fantasia Database, which contains 40 subjects, including two groups of 20 young subjects (21‒34 years old) and 20 older subjects (68‒85 years old). We used two non-linear methods, Poincare and Recurrence Quantification Analysis (RQA), to determine how different age groups affect HRV using Matlab and Kubios software.

By analyzing some features extracted from this non-linear method based on a mathematical model and making a comparison, the results indicate that the SD1, SD2, SD1/SD2, and area of an ellipse (S) in Poincare will be lower in old people than in young people, but %REC, %DET, Lmean and Lmax will recur more often in older people than in younger ones. Poincare Plot and RQA show opposite correlations with aging. In addition, Poincaré's plot showed that young people have a greater range of changes than the elderly.

According to the result of this study, heart rate changes can be reduced by aging, and ignoring this issue could lead to cardiovascular disease in the future (Tab. 3, Fig. 7, Ref. 55).