Introduction of Application of Gini Coefficient to Heart Rate Variability Spectrum for Mental Stress Evaluation

Abstract

Background: The Gini coefficient is a statistical tool generally used by economists to quantify income inequality. However, it can be applied to any kind of data with unequal distribution, including heart rate variability (HRV). Objectives: To assess the application of the Gini coefficient to measure inequality in power spectral density of RR intervals, and to use this application as a psychophysiological indicator of mental stress. Methods: Thirteen healthy subjects (19 ± 1.5 years) participated in this study, and their RR intervals were obtained by electrocardiogram during rest (five minutes) and during mental stress (arithmetic challenge; five minutes). These RR intervals were used to obtain the estimates of power spectral densities (PSD). The limits for the PSD bands were defined from 0.15 to 0.40 Hz for high frequency band (HF), from 0.04 to 0.15 Hz for low frequency band (LF), from 0.04 to 0.085 Hz for first low frequency sub-band (LF1) and from 0.085 to 0.15 Hz for second low frequency sub-band (LF2). The spectral Gini coefficient (SpG) was proposed to measure the inequality in the power distribution of the RR intervals in each of above-mentioned HRV bands. SpG from each band was compared with its respective traditional index of HRV during the conditions of rest and mental stress. All the differences were considered statistically significant for p < 0.05. Results: There was a significant decrease in HF power (p = 0.046), as well as significant increases in heart rate (p = 0.004), LF power (p = 0.033), LF2 power (p = 0.019) and LF/HF (p = 0.002) during mental stress. There was also a significant increase in SpG(LF) (p = 0.009) and SpG(LF2) (p = 0.033) during mental stress. Coefficient of variation showed SpG has more homogeneity compared to the traditional index of HRV during mental stress. Conclusions: This pilot study suggested that spectral inequality of Heart Rate Variability analyzed using the Gini coefficient seems to be an independent and homogeneous psychophysiological indicator of mental stress. Also, HR, LF/HF, SpG(LF) of HRV are possibly important, reliable and valid indicators of mental stress.

Glycine Receptor Inhibition Differentially Affect Selected Neuronal Populations of the Developing Embryonic Cortex, as Evidenced by the Analysis of Spontaneous Calcium Oscillations

“…Applications of this coefficient include the analysis of electrocardiogram (ECG) signals [ 42 ], electroencephalogram (EEG) traces [ 43 ], functional magnetic resonance imaging [ 44 ] and gene expression analysis [ 45 ]. Interestingly, the use of the Gini coefficient for the analysis of ECG signal have allowed it to unveil the relationship between ECG changes and psychological stress [ 42 ]. This is remarkable as it contributes to obtaining a correlated readout of electrical activity and a cognitive process.…”

Variabilidade da Frequência Cardíaca como biomarcador do estresse: revisão integrativa

“…A redução da VFC está associada ao estresse e, na maioria dos estudos, as variáveis sofreram alterações em resposta ao estresse induzido por vários métodos. A diminuição nos parâmetros da VFC foi verificada nos estudos encontrados, entre eles, os de Leme, et al, (2015), Peçanha, et al, (2017aPeçanha, et al, (2017b); Rimes, et al, (2017) As alterações da VFC, especificamente no que se refere à ativação parassimpática reduzida, foram vistas por meio do aumento na razão LF/HF e Potência de LF (Sánchez-Hechavarría, et al 2019), e as variáveis do domínio do tempo mostraram-se aumentadas em relação aos valores de referência nos estudos de Naranjo, et al, (2015) representado atividade parassimpática.…”

“…Também foram estudados o estresse metal e a aritmética mental (Sánchez-Hechavarría, et al, 2019;Zanetti, et al, 2019), o protocolo de estresse: linha de base, teste Go/NoGo, recuperação, posição supina e ortostase (Mestanikova, et al, 2019) e o Teste de Simulação de Falar em Público (TSFP) (Rimes, et al, 2017). O estresse pós-traumático foi identificado por meio de avaliação clínica (Reinertsen, et al, 2017).…”

“…A VFC foi registrada usando um aparelho de eletrocardiograma (ECG) (Borchini, et al, 2015;Grantcharov, et al, 2018;Javorka, et al, 2017;Markov, et al, 2016;Mestanikova, et al, 2019;Peçanha, et al, 2017a;Peçanha, et al, 2017b;Reinertsen, et al, 2017;Sánchez-Hechavarría, et al, 2019;Tonhajzerova, et al, 2016;Visnovcova, et al, 2015;Zanetti, et al, 2019). A duração da gravação, nesses estudos, variou duas sessões de monitoramento de ECG de 24 horas.…”

“…As variáveis do domínio do tempo foram analisadas nos estudos (Borchini, et al, 2015;Figueiredo, et al, 2016;Grantcharov, et al, 2018;Leme, et al, 2015;Markov, et al, 2016;Naranjo, et al, 2015;Peçanha, et al, 2017a;Peçanha, et al, 2017b;Proietti, et al, Research, Society and Development, v. 9, n. 12, e34991211125, 2020(CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v9i12.11125 2017Rimes, et al, 2017) e as mais utilizadas foram: desvio-padrão dos intervalos RR (SDNN); raiz quadrada da média do quadrado das diferenças entre os intervalos RR normais adjacentes (RMSSD) e porcentagem dos intervalos RR adjacentes ( pNN50). No domínio da frequência, as bandas de baixa frequência (LH), alta frequência (HF) e a razão entre LF/HF foram as mais utilizadas (Figueiredo, et al, 2016;Grantcharov, et al, 2018;Markov, et al, 2016;Proietti, et al, 2017;Sánchez-Hechavarría, et al, 2019). Quanto aos métodos não lineares, foram utilizados os parâmetros do gráfico Poincaré (SD1 e SD2), a entropia de amostra (SampEn) (Naranjo, et al, 2015) e a Detrended Fluctuation Analysis (DFA) (Šapina, et al, 2018).…”