Engenharia Biomédica
URI permanente para esta coleçãohttps://repositorioacademico.universidadebrasil.edu.br/handle/123456789/915
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Item Uso de redes complexas ordinais para análise de variabilidade da frequência cardíaca(Universidade Brasil, 2022) Costa, Gilberto de Araújo; Santos, Laurita dos; Azevedo, Francisco Honeidy CarvalhoIntroduction: Efforts to apply the analysis of complex ordinal networks are being expanded and recognized as effective in the evaluation and interpretation of non-linear biological signals, including those obtained from electrocardiograms (ECGs) and electroencephalograms (EEGs). Ordinal symbolic analysis, that is, the analysis of ordinal representations, has some practical advantages. First of all, it is generally recognized as being conceptually simple and computationally fast. Furthermore, ordinal patterns, being defined by inequalities, are relatively robust against observational noise. For this reason, ordinal symbolic analysis has remained a popular method in biology and medicine, especially when it comes to distinguishing normal and abnormal health conditions in real time. Objective: To analyze the quantification measures of complex networks to apply them in the differentiation of time series of RR intervals of a group of healthy individuals and those with coronary artery disease. Material and Methods: This is a computational study that involves analysis of time series of RR intervals of healthy young people with coronary artery disease from a database. The time series was mapped for transformation into complex networks. From the complex networks, several parameters were obtained, such as entropy calculation, number of network edges, average degree of network, network density, and global clustering coefficient of the network. In the implementation of the mathematical analyses, Python and SPSS version 20.0 were used for the statistical analyses. In the first part of the work, the impact of dimension and delay parameters on entropy measurements was analyzed. In the second part, the total sample corresponding to 40time series of RR intervals, divided into two groups, were compared by applying the network quantification measures. Results and Discussion: The parameters had a directly proportional effect on entropy measurements up to a certain limit. Findings in the literature showed that the limitation in the analysis of ordinal networks is caused by the dissipation in the mapping process or by the lack of evidence of some types of signals. It was observed that to reduce the degenerations, the dimensions need to be large enough without, however, derail the computational analysis or excessively increasing the noise in the signals. In the quantifiers of the network, it was possible to evaluate the impact of the parameters on the efficacy, for the classification of healthy and coronary patients. Conclusion: Shannon entropy stood out in all dimensions for delay 6, showing better results in dimensions 5 and 6, with special emphasis on dimension 6 in several of them. Enabling the differentiation of time series of RR intervals of a group of healthy and coronary diseased individuals.Item Desenvolvimento de detector de baixo custo para captação e análise de séries temporais de intervalos RR(Universidade Brasil, 2024) Rizzato, Fernando Kendy Aoki.; Santos, Laurita dosDigital 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.