Linear and nonlinear analysis of heart rate variability in healthy subjects and after acute myocardial infarction in patients

Test-retest reliability of heart-rate variability metrics in individuals with aphasia

Individuals with aphasia may have impairments in cognition, higher incidences of depression, and a variety of post-stroke functional impairments. However, evaluating these issues is challenging since most assessments require some degree of linguistic processing and task instructions are often verbal. Heart rate variability has shown to be an objective marker for cognitive functioning in neurologically healthy individuals, depression and post-stroke depression, and post-stroke functional impairments. However, before the utility of heart rate variability is established in persons with aphasia, its test-retest reliability needs to be established. The purpose of this study was to assess test-retest reliability of heart rate variability metrics in persons with aphasia. Heart rate variability was recorded at rest while in a sitting position in twenty-one persons with aphasia at two time points. Heart rate variability metrics were mostly moderately reliable. The majority of participants had resting heart rate variability parasympathetic and sympathetic indexes similar to those of neurologically healthy individuals. Further research is needed to establish the test-retest reliability of heart rate variability metrics in different postures and breathing conditions in persons with aphasia.

The novel approach of temporal dependency complexity analysis of heart rate variability in obstructive sleep apnea

Obstructive sleep apnea (OSA) is a serious sleep disorder, which leads to changes in autonomic nerve function and increases the risk of cardiovascular disease. Heart rate variability (HRV) has been widely used as a non-invasive method for assessing the autonomic nervous system (ANS). We proposed the two-dimensional sample entropy of the coarse-grained Gramian angular summation field image (CgSampEn_2D) index. It is a new index for HRV analysis based on the temporal dependency complexity. In this study, we used 60 electrocardiogram (ECG) records from the Apnea-ECG database of PhysioNet (20 healthy records and 40 OSA records). These records were divided into 5-min segments. Compared with the classical indices low-to-high frequency power ratio (LF/HF) and sample entropy (SampEn), CgSampEn_2D utilizes the correlation information between different time intervals in the RR sequences and preserves the temporal dependency of the RR sequences, which improves the OSA detection performance significantly. The OSA screening accuracy of CgSampEn_2D (93.3%) is higher than that of LF/HF (80.0%) and SampEn (73.3%). Additionally, CgSampEn_2D has a significant association with the apnea-hypopnea index (AHI) (R = -0.740, p = 0). CgSampEn_2D reflects the complexity of the OSA autonomic nerve more comprehensively and provides a novel idea for the screening of OSA disease.

Interictal Heart Rate Variability Analysis Reveals Lateralization of Cardiac Autonomic Control in Temporal Lobe Epilepsy

Purpose: The temporal lobe, a critical hub for cognition, also plays a central role in the regulation of autonomic cardiovascular functions. Lesions in this area are usually associated with abnormalities in the regulation of heart rate (HR) and blood pressure (BP). The analysis of the heart rate variability (HRV) is useful to evaluate the cardiac parasympathetic nervous system activity. This study aims at comparing HRV changes occurring in two groups of patients suffering from Temporal Lobe Epilepsy (TLE). To that aim, we evaluated patients differentiated by the right or left location of the epileptic foci.

Materials and Methods: Fifty-two adult patients with a diagnosis of TLE were enrolled. Each patient underwent a 20-min EEG + EKG recording in resting state. According to the localization of epileptic focus, patients were divided into two subgroups: right TLE (R-TLE) and left TLE (L-TLE). HRV parameters were calculated with a short-lasting analysis of EKG recordings. Time-domain and frequency domain-related, as well as non-linear analysis, parameters, were compared between the two groups.

Results: Compared to the R-TLE group, L-TLE subjects showed a significant decrease in low frequency (LF) (p < 0.01) and low frequency/high-frequency ratio (LF/HF) (p < 0.001) as well as increased HF values (p < 0.01), a parameter indicative of the presence of an increased cardiac vagal tone. These results were also confirmed in the subgroup analysis that took into account the seizure types, responses to antiepileptic drugs, seizure frequencies, and etiology.

Conclusions: The main finding of the study is that, compared to R-TLE, L-TLE is associated with increased cardiac vagal tone. These results indicate that patients with TLE exhibit a lateralized cardiac autonomic control. L-TLE patients may have a lower risk of developing cardiac dysfunctions and less susceptible to develop Sudden Death for Epilepsy (SUDEP).

[Decreased Heart Rate Variability in Sickle Cell Anemia as Effect of Pulmonary Arterial Hypertension]

Decreased heart rate variability (HRV) is associated with increased mortality risk in various diseases. The objective of this investigation:to study HRV in patients with sickle cell anemia (SCA) and to assess the effect of pulmonary arterial hypertension (PAH) on HRV in these patients. Materials and methods. HRV registration and Doppler echocardiographic assessment of systolic pulmonary arterial pressure (PAP) was carried out in 61 stable patients with SCA and 24 healthy subjects. Results. Low frequency power (LFP) and high frequency power (HFP) were decreased in SCA patients compared to healthy subjects. Among SCA patients, PAH patients had lower  LFP and HFP than patients without PAH. In SCA patients, systolic PAP showed significant negative correlation with LFP and HFP. Conclusion. HRV is significantly decreased in SCA patients, especially in those with PAH. HRV may be particularly useful in early detection of PAH patients who may have worse prognosis and higher mortality risk.

Fractal analysis of heart rate variability as a predictor of mortality: A systematic review and meta-analysis

Previous studies have suggested benefits of applying fractal analysis to intervals between R waves in electrocardiography as an additional prognostic marker. The aim of this study was to investigate whether fractal analysis can provide an independent predictor of cardiac mortality or all-cause mortality. Prognostic cohort studies reporting fractal heart rate variability results from 24-h Holter monitor recordings were selected for comparison. Populations were subdivided into four groups-post-myocardial infarction, left ventricular dysfunction, other cardiac, and non-cardiac patients-and analysed using ANOVA, Forest plots (using pooled mean difference), and Funnel plots. The most significant mean differences were recorded in short-term fractal self-similarity (α₁) (-0.17, 95% CI [-0.21, -0.13], p < 0.00001) and the traditional measure called standard deviation of NN intervals (SDNN) (-13.31, 95% CI [-18.89, -7.73], p < 0.00001) between the deceased and survivor groups. Fractal measures of long-term fractal self-similarity (α₂), 1/f scaling (β), and traditional heart rate variability measures of high frequency to low frequency ratio show promise. This review indicated that fractal measure α₁ and traditional measure SDNN could be potential predictors of mortality, but require further assessment to determine appropriate thresholds for clinical significance and additional targeted prognostic studies to properly define their applicability as prognostic markers. Therefore, clinicians should interpret fractal and traditional measures with caution since such measures have yet to be fully described as biomarkers for clinical application.

Nonlinear analyses of heart rate variability in hypertension

OBJECTIVE

The autonomic nervous system plays an important role in blood pressure regulation and in the development of hypertension. Heart rate variability (HRV) may be of importance in identifying subjects at higher risk of developing hypertension. In the present study, comparative analysis of HRV for normal and hypertension subjects using nonlinear indices has been carried out.

PATIENTS AND METHODS

We analyzed the data from 24 patients with essential hypertension aged 45-55 (HG). All the subjects in this group had a confirmed diagnosis of the first- or second-stage essential hypertension. The control group for these subjects included 32 healthy volunteers of the same age group (CG). We analyzed the whole 5minutes of the ECG recording. Nonlinear indices D₂, K₂ and lagged Poincaré plot indices were calculated.

RESULTS

Values of D₂ and K₂ in hypertension state is statistically significantly lower than in healthy. Poincaré plot indices SD1 and SD2 in healthy volunteers are statistically higher than in hypertensive patients for all lags (P<0,05). SD1/SD2 ratio is also higher in healthy subjects, but the difference is statistically significant only for l=5 and 6.

CONCLUSION

The heart rhythm in groups with hypertension is more regular, its dynamics are less complex and less "chaotic," compared to healthy ones. This is expressed in lower values of entropy and correlation dimension. The HR stabilization is observed at both short and long time intervals, but mainly due to the decrease in the difference of the "neighboring" R-R intervals.

Can RR intervals editing and selection techniques interfere with the analysis of heart rate variability?

BACKGROUND

Oscillation between successive sinus beats or RR intervals, termed heart rate variability, is an important marker of autonomic function of the heart. However, its analysis may be influenced by the database recorded based on the occurrence of interference.

OBJECTIVE

To evaluate if the techniques of identification and editing of artifacts, as well as the selection methods of RR intervals, can interfere with heart rate variability analysis.

METHODS

The RR intervals of 56 subjects (30 aortic stenosis patients, 14 physically active individuals, 12 amateur athletes) were recorded for 10min using a heart rate monitor. Values with differences greater than 20%, higher than three standard deviations or outside of the normal curve (95% confidence interval) were considered artifacts. These points were corrected through data replacement, adjacent, linear and polynomial interpolation, or excluded. Then, the 256 highest stability points and the last 5min of recordings were chosen. The software programs, Kubios HRV and GraphPAD, were used to calculate and to analyze the indices of heart rate variability, respectively.

RESULTS

Strong agreement was observed among the identification algorithms; there was no difference between the correction techniques (p=0.95); and the selection methods exhibited different sections (p<0.01) with a direct influence on approximated entropy (p<0.05).

CONCLUSION

With short-term recordings, selection methods may interfere with the non-linear heart rate variability analysis. The confidence interval, the replacement by the average of previous data and the selection of 256 of the highest stability points of the signal seem to be the most adequate procedures to treat the data with prior to analysis.

Informative Nature and Nonlinearity of Lagged Poincaré Plots Indices in Analysis of Heart Rate Variability

Lagged Poincaré plots have been successful in characterizing abnormal cardiac function. However, the current research practices do not favour any specific lag of Poincaré plots, thus complicating the comparison of results of different researchers in their analysis of heart rate of healthy subjects and patients. We researched the informative nature of lagged Poincaré plots in different states of the autonomic nervous system. It was tested in three models: different age groups, groups with different balance of autonomous regulation, and in hypertensive patients. Correlation analysis shows that for lag l = 6, SD1/SD2 has weak (r = 0.33) correlation with linear parameters of heart rate variability (HRV). For l more than 6 it displays even less correlation with linear parameters, but the changes in SD1/SD2 become statistically insignificant. Secondly, surrogate data tests show that the real SD1/SD2 is statistically different from its surrogate value and the conclusion could be made that the heart rhythm has nonlinear properties. Thirdly, the three models showed that for different functional states of the autonomic nervous system (ANS), SD1/SD2 ratio varied only for lags l = 5 and 6. All of this allow to us to give cautious recommendation to use SD1/SD2 with lags 5 and 6 as a nonlinear characteristic of HRV. The received data could be used as the basis for continuing the research in standardisation of nonlinear analytic methods.

Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.

Influence of Heart Rate in Non-linear HRV Indices as a Sampling Rate Effect Evaluated on Supine and Standing

The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position (p < 0.05), concomitant with a 28% increase in mean HR (p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes.

Fractal correlation property of heart rate variability in response to the postural change maneuver in healthy women

Background

We evaluated the effects of the PCM on the fractal analysis of the HRV in healthy women

Method

We evaluated healthy women between 18 and 30 years old. HRV was analyzed in the time (SDNN, RMSSD, NN50 and pNN50) and frequency (LF, HF and LF/HF ratio) domains as well as short and long-term fractal exponents (alpha-1 and alpha-2) of the detrended fluctuation analysis (DFA). HRV was recorded at rest for ten minutes at seated rest and then the women quickly stood up from a seated position in up to three seconds and remained standing for 15 minutes. HRV was recorded at the following time: rest, 0–5 min, 5–10 min and 10–15 min during standing.

Results

We observed decrease (p < 0.05) in the time-domain indices of HRV between seated and 10–15 minutes after the volunteer stood up. The LF (ms²) and HF (ms²) indices were also reduced (p < 0.05) at 10–15 minutes after the volunteer stood up compared to seated while the LF (nu) was increased at 5–10 min and 10–15 min (p < 0.05). The short-term alpha-1 exponent was increased (p < 0.05) at all moments investigated compared to seated. Increase in the properties of short-term fractal correlations of heart rate dynamics accompanied by a decrease in the parasympathetic modulation and global HRV was observed in response to the postural change maneuver.

Conclusion

We suggest that fractal analysis of HRV is more sensitive than frequency and time-domain analysis of HRV during the postural change maneuver.