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 into different frequencies. This transformation gives numerical values about their relative intensity. 8,9 Spectral methods produce a decomposition of total variation of a data series into its frequency components, which can be expressed in the form of a spectral density function that depicts spectral power as a function of frequency. 10 A standard for HRV measurement and interpretation of frequency domain variables was published in 1996, and most subsequent studies are based on it. 4,9 These traditional HRV indices in frequency domain variables include very low frequency [0.0033-0.04 Hz], HF [0.15-0.4 Hz] and LF [0.04-0.15 Hz]. HF has been linked to the parasympathetic influence on the heart, while LF is modulated by baroreflex activity and has been linked to both sympathetic and parasympathetic activity. 4,6,7,11,12 The power of traditional HRV indices in different bands changes by increasing or decreasing sympathetic or vagal modulation. However, it is unknown how equally this power in each frequency band is distributed during rest. It is also unknown how this power distribution gets affected with changes in sympathetic or parasympathetic modulation. To our best knowledge, the inequality in power distribution of HRV spectrum has not been measured before. Therefore, the present study aims 1) to apply the Gini coefficient to power spectral density of HRV to measure the inequality of power distribution of frequency bands; 2) to compare the inequality in power spectrums of HRV signals during rest versus under mental stress; 3) to evaluate the Gini coefficient as a psychophysiological indicator of mental stress in comparison to traditional HRV indices. Methods Study population A total of 13 healthy subjects (7 females, 6 males), age 19 ± 1.5 years, BMI 22.3 ± 1.3 kg/m 2 , participated in this crossover study. An a-priori power analysis found that this number of participants would yield 80% power at an alpha level of 0.05. All the subjects were non-smokers and had no history of heart disease, systemic hypertension or any other disease. Participants did not take any medications, drugs or alcohol for 12 hours preceding the experiment and were advised not to drink any caffeinated beverages on the morning of the study. Prior to participation, subjects signed an informed consent. Study procedures were in accordance with the Declaration of Helsinki and the study protocol was approved by the Ethics Committee of the Medical University of Santiago de Cuba. Experiments were performed in a quiet environment, between 9 a.m. and 12:30 p.m.. ECGs were taken in a sitting position, during rest and during arithmetic mental stress. After attachment of the electrodes, every subject relaxed for 10 min. ECG recordings were obtained during rest with spontaneous breathing for 5 min. Immediately afterwards, subjects performed a mental arithmetic task for 5 min. 13-15 The mental arithmetic task is one of the most efficient stimuli for inducing mental stress. 16-18 Briefly, subjects subtracted 7, starting from 1000. They were instructed to subtract as accurately as possible. For a single subtraction, time allowed was 5s and was signaled by a sound. Subjects said the result aloud and after each answer, subjects received verbal confirmation (“right” or “wrong”). They continued successive subtraction, even when the result was wrong. Aside from verbalization of the answers, subjects did not talk during the mental arithmetic challenge. Signal acquisition and processing A PowerLab Acquisition System 8 ® (ADInstruments) was used to collect the ECG recordings, with a sampling rate of 1000 Hz. A standard Lead II was used for ECG measurement. The Sabarimalai-Manikandan’s 19 algorithm was used to detect the QRS complexes in the ECG signal, from which RR intervals were obtained . Pre-processing of RR series data was required before HRV analysis in order to reduce analytic errors. The standard deviation filter with percentage filter, with value of 20% from the previous interval, were used to detect ectopic intervals. 20 Cubic Spline Replacement was employed to replace ectopic intervals using cubic spline interpolation. 21 Finally, in other analysis of ECG signals, an ECG-derived Respiration Rate (EDR) was computed from raw ECG throughout the procedure via a built-in algorithm of Kubios HRV Premium ® 3.0.2 software. The algorithm examined the alterations in the amplitude of the R-peak caused by chest movements during each respiratory cycle. Under stationary conditions (i.e., short‑term registrations), the EDR is considered a reliable index of respiratory rates. 22 A previous study found a reasonable agreement between EDR and a reference respiratory rate derived from nasal/oral airflow. 23 Heart rate variability analysis Using the algorithm described by Berger, 24 the RR interval sequence was transformed into temporal RR sequence. Pre‑processed temporal 5-min RR series were subjected to spectral analysis using the Welch periodogram method to obtain the estimates of power spectral densities (PSD). A total of 2048 samples (5-min RR series) were subjected to computation through the Welch modified periodogramwith a Hann window, using segments of 512 samples and overlapping periods of 256 samples. The limits for the spectral HRV bands were delimited from 0.15 to 0.40 Hz for the HF, from 0.04 to 0.15 Hz for the LF, from 0.04 to 0.085 Hz for the LF1 and from 0.085 to 0.15 Hz for the LF2. Absolute PSD were calculated as the integral of each one-sided quadratic spectrogram in the frequency ranges previously defined. Proposed Spectral Gini HRV Indices The Gini coefficient is typically used by economists to measure income inequality. If the income level of the th [ = 1, 2. . . ] house is , the Gini coefficient is calculated using the following equation: 25 G( ) = Σ = 1 2 Σ = 1 Σ = 1 – If the incomes of all houses are equal, that is, 1 = 2 = ⋅⋅ ⋅ = , the Gini coefficient becomes 0. Additionally, when only one house has income, that is, 1 > 2 = ⋅⋅⋅ = = 0, the 726