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.
Purpose: The aim of the present study was to investigate the role of occlusion time in dynamic changes of autonomic activation during reactive hyperemia. Methods: Healthy subjects (n = 30) in the age range of 18–25 years participated in this study. Vascular reactivity was assessed by measuring the dynamic changes in finger pulse volume amplitude (PVA) and pulse transit time relative to the RR intervals in the test (occluded arm) and control arm (contralateral non-occluded arm) during 1, 3 and 5 minute of occlusion using two separate Photoplethysmographic sensors. Heart Rate Variability was computed from a simultaneously acquired ECG signal to monitor the dynamic changes in cardiac autonomic nervous activity. Time-varying analysis of all signals were shown every 1 second in average response graphs. Results: Time-varying analysis of vascular and autonomic response during reactive hyperemia demonstrated the presence of a characteristic response pattern with an increase in the Sympathetic index and a decrease in Parasympathetic index at 8 to 10 seconds, an increase in heart rate at 20 seconds and a progressive increase in PVA during the first 60 seconds after occlusion regardless of the time spent in the procedure. Moreover, a decrease in pulse transits time relative to RR intervals, followed by an increase regardless of the occlusion time was evidenced. Conclusions: Early cardiovascular sympathetic activation is independent of occlusion time during reactive hyperemia, which suggests this is a vascular autonomic reflex response involved in the generation of the physiological phenomenon of reactive hyperemia.
Background: Predicting beat-to-beat blood pressure has several clinical applications. While most machine learning models focus on accuracy, it is necessary to build models that explain the relationships of hemodynamical parameters with blood pressure without sacrificing accuracy, especially during exercise. Objective: The aim of this study is to use the RuleFit model to measure the importance, interactions, and relationships among several parameters extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals during a dynamic weight-bearing test (WBT) and to assess the accuracy and interpretability of the model results. Methods: RuleFit was applied to hemodynamical ECG and PPG parameters during rest and WBT in six healthy young subjects. The WBT involves holding a 500 g weight in the left hand for 2 min. Blood pressure is taken in the opposite arm before and during exercise thereof. Results: The root mean square error of the model residuals was 4.72 and 2.68 mmHg for systolic blood pressure and diastolic blood pressure, respectively, during rest and 4.59 and 4.01 mmHg, respectively, during the WBT. Furthermore, the blood pressure measurements appeared to be nonlinear, and interaction effects were observed. Moreover, blood pressure predictions based on PPG parameters showed a strong correlation with individual characteristics and responses to exercise. Conclusion: The RuleFit model is an excellent tool to study interactions among variables for predicting blood pressure. Compared to other models, the RuleFit model showed superior performance. RuleFit can be used for predicting and interpreting relationships among predictors extracted from PPG and ECG signals.
Introduction: Gini coefficient (Gini index or Gini ratio) is a parameter that is normally used in economy to measure the income distribution in a country or in the whole wide world, but it can be used to measure any kind of distribution. In the present study it is exposed an innovative proposal of application of the Gini coefficient to Heart Rate Variability (HRV) like a psychophysiological indicator of mental stress. Purpose: To assess the application of the Gini coefficient as a psychophysiological indicator of mental stress. Methods: The involved participants are 13 healthy individuals (age 19 ± 1.5 years). Heart rate was continuously recorded at rest (5 minutes) and during a mental stress (5 minutes). Linear and nonlinear methods of heart rate variability were assessed, and 2 new indicators (Sequential and Non-Sequential Gini) were calculated and proposed to measure HRV differences between states. Results: When comparing rest and mental stress conditions, a sensible decrease of the traditional indicators of the HRV was founded (p<0.05), an increase of the heart rate (p=0.004) and of the Sequential Gini (p=0.004) and Non-Sequential Gini (p=0.04). Conclusions: The results suggest that temporary inequality of the RR intervals analyzed from the Gini coefficient could be an adequate indicator of sympathetic activity present during the mental stress, with great potentialities with the objective to assess the consequences of psychosomatic affections and anxiety disorders.
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