RESUMO
Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic. The different waves of contagious were responsible for the increase in the number of cases that, unfortunately, many times lead to death. In this paper, we aim to characterize the dynamics of the six waves of cases and deaths caused by COVID-19 in Rio de Janeiro city using techniques such as the Poincaré plot, approximate entropy, second-order difference plot, and central tendency measures. Our results reveal that by examining the structure and patterns of the time series, using a set of non-linear techniques we can gain a better understanding of the role of multiple waves of COVID-19, also, we can identify underlying dynamics of disease spreading and extract meaningful information about the dynamical behavior of epidemiological time series. Such findings can help to closely approximate the dynamics of virus spread and obtain a correlation between the different stages of the disease, allowing us to identify and categorize the stages due to different virus variants that are reflected in the time series.
RESUMO
Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.
Assuntos
Eletrocardiografia , Frequência Cardíaca , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Processamento de Sinais Assistido por Computador , Fatores de TempoRESUMO
The heart rate variability (HRV) is an indicator of the subject homeostasis alterations. For a healthy individual, the HRV shows a nonlinear behavior, thus requiring a nonlinear approach to provide additional information about HRV dynamics. In this work, the nonlinear techniques, central tendency measure (CTM) and second-order difference plot, are applied to HRV analysis using the successive difference of RR intervals in a time series. In total are analyzed 170 tachograms collected by Polar monitor and then classified into three groups according to a cardiologist: healthy young adults, adults in preoperative evaluation for coronary artery bypass grafting for severe coronary disease and premature newborns. This approach identified the tachograms with high and low variability, which demonstrates the ability of CTM to classify and quantitatively characterize cardiac RR intervals.