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.

In this paper the algorithm for ΔρDCCA statistical test (Guedes et al., 2018) [1] is presented. Our test begins with the simulation of four time series pairs, by an ARFIMA process. These time series has N=250, 500, 1000, and 2000 points, see Guedes et al. (2018) [1]. The probability distribution function (PDF) is made available for all 10,000 samples, that start from the original time series, in supplementary material.

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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 epilepitc discharge. One of the most widely used method for to analyse long memory processes is the detrended fuctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA. Results In this paper, 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 paper. 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 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: (i) the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the the frontal channels; and (ii) the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels. Availability The functions used in this paper are available in the R package DFA (Mesquita et al., 2020). Supplementary information Supplementary information are available at Bioinformatics online.

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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.

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