Heart rate variability in subthreshold depression and major depressive disorder
Yinliang TanMeihong ZhouJiuju WangYanping SongQiang LiZetao HuangYing LiYuxin WangJingbo ZhangWenxiang QuanJu TianLina YinWentian DongBaohua Liu
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Depression
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Heart rate variability (HRV) is an important marker of cardiac autonomic modulation. Metabolic syndrome (MetS) can alter cardiac autonomic modulation, raising the risk of cardiovascular disease (CVD). Poincaré plot analysis (PPA) is a robust scatter plot-based depiction of HRV and carries similar information to the traditional HRV measures. However, no prior studies have examined the relationship between PPA and traditional HRV measures among different risk levels of MetS. We evaluated the association between the Poincare plot and traditional heart rate variability indices among adults with different risk levels of MetS.We measured anthropometric data and collected fasting blood samples to diagnose MetS. The MetS risk was assessed in 223 participants based on the number of MetS components and was classified as control (n=64), pre-MetS (n=49), MetS (n=56), and severe MetS (n=54). We calculated the Poincaré plot (PP) and traditional HRV measures from a 5 min HRV recording.Besides the traditional HRV measures, we found that various HRV indices of PPA showed significant differences among the groups. The severe MetS group had significantly lower S (total HRV), SD1 (short-term HRV), SD2 (long-term HRV), and higher SD2/SD1. The values of S, SD1, SD2, and SD2/SD1 were significantly correlated with most traditional HRV measures.We found gradual changes in HRV patterns as lower parasympathetic and higher sympathetic activity alongside the rising number of MetS components. The HRV indices of PPA integrating the benefits of traditional HRV indices distinguish successfully between different risk levels of MetS and control subjects.
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Background: Heart rate variability (HRV) is an important indicator of autonomic modulation of cardiovascular function. Diabetes can alter cardiac autonomic modulation by damaging afferent inputs, thereby increasing the risk of cardiovascular disease. We applied nonlinear analytical methods to identify parameters associated with HRV that are indicative of changes in autonomic modulation of heart function in diabetic patients. Objective: We analyzed differences in HRV patterns between diabetic and age-matched healthy control subjects using nonlinear methods. Methods: Lagged Poincare plot, autocorrelation, and detrended fluctuation analysis were applied to analyze HRV in electrocardiography (ECG) recordings. Results: Lagged Poincare plot analysis revealed a decrease in the standard deviation of instantaneous beat-to-beat interval variability (SD1) and in the ratio of SD1 to the continuous long-term R-R interval variability (SD12) in the diabetic group, indicating a decrease in heart rate parasympathetic modulation. The detrended fluctuation exponent derived from long-term fitting was higher than the short-term one in the diabetic population, which was also consistent with decreased parasympathetic input. The autocorrelation function of the deviation of inter-beat intervals exhibited a highly correlated pattern in the diabetic group compared with the control group. Conclusion: The HRV pattern significantly differs between diabetic patients and healthy subjects. All three statistical methods employed in the study may prove useful to detect the onset and extent of autonomic neuropathy in diabetic patients. (Arq Bras Cardiol. 2013; [online].ahead print, PP.0-0)
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The aim of this research is to test the effects of reading and listening to heavenly religious books on HRV (heart rate variability). To do this research, the ECG signal was recorded from the volunteers during four stages, and HRV signal were obtained. Then 12 features, including time domain, frequency domain, and nonlinear analysis, were extracted for each volunteer. By calculating P-value for each interference stage compared to the resting stage, we extracted four various features from HRV that changed during the interference. The P-values of these features were below 0.065.These 4 significant features of HRV were as follows: 1) LF: low frequency of HRV signal. 2) RMSSD: the root-mean square difference of successive R-R intervals. 3) SDNN: a standard deviation of the NN intervals, which is the square root of their variance. 4) SD1: one of the indices obtained from Poincare plot.According to the result, RMSSD, SDNN, and SD1 were significantly decreased and LF was significantly increased during the test. The results showed that the ANS (autonomic nervous system) activity in volunteers was changed during the test. This shows the mental or emotional activity when the volunteers are reading and listening to the holy books.
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Высокая выживаемость детей после лечения по поводу опухоли головного мозга (ОГМ) требует разработки новых подходов к их реабилитации и адаптации. Вариабельность ритма сердца (ВРС) является информативным индикатором нарушения как вегетативной, так и центральной регуляции работы сердца и в целом всего организма. Цель исследования заключалась в сравнении частотных (спектральных), временных и нелинейных показателей ВРС, у здоровых детей младшего школьного возраста и детей, находящихся в ремиссии после нейроонкологического заболевания. Материалы и методы. У двух групп детей 7–11 лет (ОГМ, n = 35, и неврологически здоровые школьники, n = 34) регистрировали фотоплетизмограмму (ФПГ) (3 мин сидя). После выделения кардиоинтервалов из кривой ФПГ проводили частотный, временной и нелинейный анализ ВРС (Kubios HRV Standard 3.5.0). Затем сравнивали группы детей по полученным показателям статистическими критериями. Результаты. Сравнение двух групп детей по тесту Манна-Уитни выявило статистически значимые различия по следующим показателям ВРС: длительность RR-интервалов, ЧСС, SDNN, RMSSD, pNN50, размеры облака Пуанкаре (SD1, SD2) и ApEn. При этом все показатели, отражающие ВРС, у детей с ОГМ были ниже, а ЧСС и значение аппроксимированной энтропии (ApEn) – выше. Показатели частотного (спектрального) анализа у детей двух групп значимо не различались. Заключение. При анализе вариабельности ритма сердца необходимо учитывать показатели, полученные разными методами. Дети, перенесшие лечение по поводу опухоли головного мозга, отличаются сниженной ВРС по ряду показателей временного и нелинейного анализа, но не различаются по спектральным характеристикам ритма сердца. The high survival rate of children after treatment for a brain tumor (BT) requires the development of new approaches to their rehabilitation and adaptation. Heart rate variability (HRV) is a sensitive indicator of a violation of autonomic and central regulation. The aim of the study was to compare the frequency domain, time domain and nonlinear indicators of HRV in healthy children of primary school age and children in remission after neuro-oncological disease. Materials and methods. In two groups of children aged 7–11 years (BT, n = 35, and neurologically healthy schoolchildren, n = 34), a photoplethysmogram (PPG) was recorded (3 min in a sitting position). After extraction of RR intervals from PPG, frequency domain (spectral), time domain and nonlinear HRV analysis (Kubios HRV Standard 3.5.0) were performed. Then two groups of children were compared according to the obtained indicators by statistical criteria. Results. Comparison of two groups of children according to the Mann-Whitney test revealed statistically significant differences in the following HRV indicators: duration of RR intervals, heart rate, SDNN, RMSSD, pNN50, Poincare plot sizes (SD1, SD2) and ApEn. All indicators reflecting variability in children with BT are lower, heart rate is higher and approximated entropy (ApEn) is higher as well. The indicators of frequency analysis do not differ significantly in children of the two groups. Conclusion. When analyzing HRV, it is necessary to take into account the indicators obtained by different methods. Children who have undergone treatment for a brain tumor are characterized by reduced HRV according to a number of indicators of time domain and nonlinear analysis, but do not differ in the spectral characteristics of the heart rhythm.
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The analysis of heart rate variability (HRV) is recognized as a powerful non-invasive tool to evaluate the influence of autonomic nervous system on the heart and the heart-brain interaction. It is well-known that relatively high HRV is correlated to health condition while low HRV corresponds to cardiovascular disease or could be caused by mental stress, depression or exercise. In order to investigate heart-brain interaction we compare linear and non linear parameters calculated from HRV signals recorded in the same subject at similar heart rate (HR) values, during two different conditions, i.e. static exercise during sailing and dynamic exercise on cycloergometer. In the study, performed in one high-performance would class dinghy sailor, the HR was recorded at rest and during the two types of exercise. For the analysis, tachogram tracts with similar HR values were considered. The power spectral densities in very-low, low (LF) and high (HF) frequency bands were evaluated as well as the LF/HF ratio, the two standard deviations (SD1 and SD2) of the Poincare plot, the beta values and the fractal dimension (FD). The results indicate a decrease of HRV, LF, HF, SD1 and SD2 parameters, as well as an increase of beta and FD during both types of exercise compared to rest. However, the higher values of LF, LF/HF ratio and SD2 as well as the lower value of FD in upwind sailing in comparison to dynamic exercise on cycloergometer, at similar HR, suggest a different sympatho-vagal modulation on cardiac function and therefore a different heart-brain interaction in these isometric and isotonic exercises.
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Human heart rate is not a constant phenomenon; it varies over time and is known as Heart rate variability (HRV). HRV is a predictor of cardiac health because several physiological activities regulate the heart rhythm. Electrocardiogram (ECG) is a non-invasive biomarker tool used to diagnose various cardiac problems by extracting hidden patterns. Cardiac health declines with age and the interbeat interval (RR) gives high-temporal resolution to assess Autonomic nervous system function in healthy patients (ANS). This study utilizes the ECG data of healthy neonates, infants, children, young adults, and middle-aged adults. The HRV indices, including the Time domain (SDNN, SDNNI, DC, AC, HRVti, TINN, Stress index), Frequency domain (VLF, LF, HF, Total power), and Nonlinear analysis (Poincare plot, Approximate entropy, Sample Entropy, Detrended fluctuation analysis, and Recurrence plot), are used to extract the linear and nonlinear physiology of the heart. The Kruskal-Wallis test is utilized to examine the correlations between these groups. The study demonstrates that the activity of the parasympathetic nervous system increases from neonates to children and then tends to diminish after 12 years. The activity of the Sympathetic nervous system tends to diminish in neonates and rise in adults. The autonomic nervous system imbalance in neonates tends to balance until age 12 and then grow. This study demonstrates how normal heart physiology's complexity, correlation, and temporal dimension vary with age.
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Major complications such as cardiac death and cardiac autonomic neuropathy are caused by diabetic autonomic neuropathy. Heart Rate Variability (HRV) analysis has shown to detect variations in the autonomic balance of heart rate and is useful for early detection of autonomic dysfunction. This study presents the outcome of HRV analysis of short ECG recordings taken from nondiabetic and type 2 diabetes patients, applying Poincaré plot indices represented by short term variation (SD1), long term variation (SD2) and complex correlation (CCM) measure which measures the temporal dynamics, for early detection of cardiac autonomic neuropathy. SD1 and the ratio SD1/SD2 were found to be significantly lower in type 2 diabetes patients than the control group. The highest discriminatory power was observed with CCM, indicating the advantage of using a dynamic measure for HRV rather than the static Poincaré plot indices. SD1 and CCM could be markers for CVD risk in type 2 diabetic patients.
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Background Heart Rate Variability is an objective index to assess cardiac autonomic function. Time-domain,frequency-domain and nonlinear analysis are the most important ways to analyze the heart rate variability. Currently,the nonlinear analysis method is becoming more and more popular.Objective Introducing the scatterplot method in the analysis of heart rate variability and its potential value in the clinic.Content The development of heart rate variability of the process,mainly various scatter plot analysis and its clinical application.Trend Scatter plot is expected as a more comprehensive and real analysis method to reflect heart rate variability,then make good sense clinically.
Key words:
Heart rate variability; Poincare plot; Clinical application
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