ABC | Volume 113, Nº4, October 2019

Original Article Sánchez-Hechavarría et al. Inequality in HRV spectrum for evaluation of mental stress Arq Bras Cardiol. 2019; 113(4):725-733 Figure 1 – Principal ComponentAnalysis of Traditional and Spectral Gini Indices of Heart Rate Variability during rest and mental stress (aHF = absolute HF; aLF1 = absolute LF1; aLF = absolute LF; aLF2 = absolute LF2). HR: heart rate; RMSSD: Root Mean Square of the Successive Differences; EDR: ECG-derived; Respiration Rate; LF: low frequency; HF: high frequency; SpG: spectral Gini coefficient. HR LF_HF SpG_LF1 SpG_HF aLF2 aLF aLF1 aHF EDR RMSSD SpG_LF2 SpG_LF Biplot 1.5 0.5 –0.5 –1 –1 –0.5 0.5 0 1 1.5 2 –1.5 0 1 Dimension 1 Dimension 2 Variables STATE = REST STATE = METAL STRESS SpG(LF2) were significant but not for SpG(LF1) during mental stress. It should also be noted that the traditional HRV index showed a significant decrease in HF power during mental stress, but the decrease in SpG(HF) was not significant. These data suggest that there was decrease in power in the HF band, but the distribution of power within the HF band remained similar during rest and mental stress. The coefficient of variation showed that, in comparison to traditional HRV indices, Gini spectral indices are homogeneous (see Table 1), meaning that the numeric values of changes in distribution of power during mental stress are located closer to the center (mean) and do not have high SD values like traditional indices. Pearson Correlation (and Spearman’s correlation) Tests revealed poor correlation values between traditional and Spectral Gini Indices during mental stress, even though LF and LF2 of traditional HRV index showed good correlation with SpG (LF2) at rest. This indicated that Gini values are independent of traditional HRV indices and contribute to the additional information not reported until now. Principal Component Analysis of traditional and Spectral Gini indices helps to reduce the multiple characteristics or variables of a sample (HRV) to a few dimensions (in this case, only two dimensions). It can be explained as trying to reduce twelve variables of an object to two values or characteristics and to determine which out of these twelve variables are the most robust for those two characteristics (two dimensions), which allow a better study of the object of interest. Dimension 2 is what differentiates the state of stress (green arrow on the figure, which tends to go upward) from the state of rest (red arrow on the figure, which tends to go below). Therefore, even though LF and HF have values >1 on Dimension 1, the variables with high load such as HR,LF/HF, SpG LF and SpG LF2 from Dimension 2 are considered physiologically and clinically more important as state indicators. ROC curve was produced in order to evaluate the efficacy of Spectral Gini indices as an evaluator of mental stress. The cutoff points of the different indicators in the differentiation of the psychophysiological states, obtained from the Youden Index of the ROC curve, can be observed. However, it stands out how the HR (p = 0.001) the LF/HF (p = 0.001) and the SpG (LF) (p = 0.011) constituted the most optimal (ROC model) and effective indicators in the discrimination between rest and mental stress with the best values of sensitivity, specificity, Youden Index and area under the curve (p < 0.05). The results shown on Table 4 are consistent with the results in Table 1, Figure 1 and Table 3, suggesting that HR; LF/HF and SpG LF were highlighted in the discrimination of the states of rest and stress. The significant increase in LF and SpG(LF) power during mental stress allows discussion on the contributing factors for LF power. It is generally accepted that the HF component 729