Desenvolvimento de detector de baixo custo para captação e análise de séries temporais de intervalos RR

Abstract

Digital health (e-Health) improves quality and life expectancy, with accessible devices like smartphones monitoring vital signs. In cardiology, due to the high mortality of cardiovascular diseases, the electrocardiogram (ECG) is crucial for assessing the heart's electrical activity and determining treatments. The prototype also applies linear and nonlinear analysis methods to characterize Heart Rate Variability (HRV), calculating indices such as RMSSD, standard deviation of heart rate, and geometric parameters of the Poincaré Plot. The system's validation involved comparing the measurements obtained by the prototype with a conventional ECG device in a population of individuals with different cardiovascular conditions. The results showed that for most time-domain and frequency-domain parameters, there were no statistically significant differences between the data collected by the prototype and the conventional ECG. However, parameters such as mean RR and mean HR showed significant differences. Linear regression analysis indicated a strong correlation between the HRV indices obtained by the prototype and the conventional method. The developed prototype proved to be an effective tool for capturing and analyzing RR intervals and HRV, providing results comparable to traditional methods and showing promise for clinical use in cardiovascular health assessment. To ensure the robustness of the data, adaptive analyses and statistical measures were conducted, utilizing Student's t-test, Mann-Whitney test, and linear regression, with a significance level of p < 0.05. This approach ensured that the conclusions drawn were based on solid and reliable evidence.