Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient.

Climate change is one of the most relevant challenges that the world has to deal with. Studies that aim to understand the behavior of environmental and atmospheric variables and the way they relate to each other can provide helpful insights into how the climate is changing. However, such studies are complex and rarely found in the literature, especially in dealing with data from the Brazilian territory. In this paper, we analyze four environmental and atmospheric variables, namely, wind speed, radiation, temperature, and humidity, measured in 27 Weather Stations (the capital of each of the 26 Brazilian states plus the federal district). We use the detrended fluctuation analysis to evaluate the statistical self-affinity of the time series, as well as the cross-correlation coefficient ρDCCA to quantify the long-range cross-correlation between stations, and a network analysis that considers the top 10% ρDCCA values to represent the cross-correlations between stations better. The methodology used in this paper represents a step forward in the field of hybrid methodologies, combining time series and network analysis that can be applied to other regions, other environmental variables, and also to other fields of research. The application results are of great importance to better understand the behavior of environmental and atmospheric variables in the Brazilian territory and to provide helpful insights about climate change and renewable energy production.

Caracterização da rede de internações por diabetes mellitus no estado da Bahia: teoria das redes / Characterization of the hospitalization network for diabetes mellitus in the state of Bahia: network theory / Caracterización de la red de hospitalización por diabetes mellitus en el estado de Bahía: teoría de redes

Este artigo tem como objetivo caracterizar a rede de internações das pessoas com Diabetes mellitus, segundo a região de saúde de residência e atendimento, no período de 2010 a 2017 no estado da Bahia. Para tanto, escolheu-se o método de estudo ecológico modelado por meio da teoria das redes, em que a população de estudo é representada pelas ocorrências de internações por diabetes em hospitais do Sistema Único de Saúde (SUS). Verificou-se que todas as regiões de saúde da Bahia apresentaram fluxo de saída e de entrada diferentes de zero em pelo menos um ano. A maior média de grau de entrada foi em Salvador e a menor em Paulo Afonso. A região com maior grau de saída foi Salvador e a menor foi Teixeira de Freitas. A maior distância média em toda a série histórica foi Teixeira de Freitas e a menor Camaçari. Assim, conclui-se que a caracterização da rede de internação pode auxiliar no processo de planejamento e diagnóstico sobre o funcionamento desta rede.

This article aimed to characterize the network of hospitalizations of people with diabetes mellitus, according to the health region of residence and care, from 2010 to 2017 in the state of Bahia. To that end, the method of ecological study modeled by the theory of networks, in which the study population is represented by the occurrences of hospitalizations for diabetes in hospitals of the Unified health system (SUS), was chosen. All health regions in Bahia showed a non-zero outflow and inflow in at least one year. The highest average inflow degree was in Salvador and the lowest in Paulo Afonso. The region with the highest outflow degree was Salvador and the lowest was Teixeira de Freitas. The longest average distance in the entire historical series was Teixeira de Freitas and the shortest, Camaçari. In conclusion, the characterization of the hospitalization network can assist in the process of planning and diagnosing the functioning of this network.

Este artículo pretende caracterizar la red de hospitalizaciones de personas con diabetes mellitus según la región de salud de residencia y atención, en el periodo de 2010 a 2017, en el estado de Bahía (Brasil). Para ello, se utilizó el estudio ecológico modelado por la teoría de redes, en el que la población de estudio son las ocurrencias de hospitalizaciones por diabetes en hospitales del Sistema Único de Salud (SUS). Se constató que todas las regiones sanitarias de Bahía tuvieron una salida y una entrada distintas de cero en al menos un año. La nota media más alta fue en Salvador, y la más baja, en Paulo Afonso. La región con mayor grado de producción fue Salvador, y la más baja, Teixeira de Freitas. La distancia media más larga de toda la serie histórica fue Teixeira de Freitas, y la más corta, Camaçari. Se concluye que la caracterización de la red de hospitalización puede ayudar en el proceso de planificación y diagnóstico del funcionamiento de esta red.

Detection of crossover points in detrended fluctuation analysis: an application to EEG signals of patients with epilepsy.

MOTIVATION: The quantification of long-range correlation of electroencephalogram (EEG) signals is an important research direction for its relevance in helping understanding the brain activity. Epileptic seizures have been studied in the past years where different non-linear statistical approaches have been employed to understand the relationship between the EEG signal and the epileptic discharge. One of the most widely used method for to analyse long memory processes is the detrended fluctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA. RESULTS: In this article, we propose: (i) two automatic approaches that successfully detect crossover points in DFA related methods on the log-log plot and (ii) a criteria to choose the reference channel for the log-amplitude function. Moreover, the DFA is applied to EEG signals of 10 epileptic patients collected from the CHB-MIT database, being the log-amplitude function used to compare the different brain hemispheres by making use of the methodology proposed in the article. The existence of long-range power-law correlations is demonstrated and indicates that the EEG signals of epileptic patients present three well-defined regions with the first region showing a 1/f noise (pink noise) for seven subjects and a random walk behaviour for three subjects. The second and third regions show anti-persistence behaviour. Moreover, the results of the log-amplitude function were divided in two groups: the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the frontal channels and the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels. AVAILABILITY AND IMPLEMENTATION: The functions used in this article are available in the R package DFA (Mesquita et al., 2020). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.