Traditional Heart Rate Variability Analysis Research Articles

Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of electrocardiogram signals: the case of intercity roads

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22–67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver’s state.

Practical R-R Interval Editing for Heart Rate Variability Analysis Using Single-Channel Wearable ECG Devices

Recent innovations in wearable devices have expanded the usage opportunities of single-channel electrocardiography (ECG) recordings in a daily life environment and enabled a variety of indirect daily activity monitoring based on heart rate variability (HRV). In general, wearable ECGs rarely undergo visual inspection by medical experts and therefore may contain noise or artifacts. Although noise/artifact-induced changes in ECG waveforms are known to cause misdetection of the QRS complex (i.e., the most distinguishable ECG components comprised of Q wave, R wave, and S wave), its complete suppression might be technically impossible. Since misdetection occurs in the QRS complex unit, we propose reframing the traditional HRV analysis flow by subdividing the R-R interval (RRI) editing into four steps in accordance with the processing detail (i.e., identification and editing) and its target unit (i.e., QRS complex or RRI). In addition, as a dubious QRS complex identification method for practical use, we utilize the amplitude at the detected point assuming the use of a single-channel wearable ECG without a reference. Initial evaluations using pseudo/real ECG datasets including ECGs with noise and artifacts show that the proposed processing/unit-based subdivision is theoretically effective for improving HRV calculation accuracy, and that the dubious QRS complex identification method for practical use also maintains this effect. Our study starting from practical HRV analysis using single-channel wearable ECG devices encourages reexamining each step in HRV analysis through the interdisciplinary research of clinical medicine and engineering/informatics that reveals the relationship of every two adjacent steps from the perspective of theory and practice.

Effect of Motion Artifact on Variation in Heart Rate Variability Parameters

The heart rate variability (HRV) is a noninvasive way properly for investigating the activity of the autonomic nervous system (ANS) as well as to predict cardiovascular diseases. To guarantee an accurate HRV analysis, a motion artifact-free HRV recording must be obtained. However, complete removal of a motion artifact is impossible when measuring heartbeats for 5 min, and the motion artifact due to sudden ANS activity must be taken into consideration for the HRV parameters. And, the ANS balance has thus far been evaluated by each individual HRV parameter calculated for a single 5 min HRV segment, leading to the dynamic activity of the ANS within the same period being ignored. Therefore, to resolve this problem, HRV parameters for ultra-short-term segments that are short enough to reflect a sudden motion artifact must be analyzed. The aim of the present study was to evaluate the effects of a motion artifact on the variation in HRV parameters to provide detailed information on ANS activity. The 121 ultra-short-term HRV segments were created by moving a 1-min window forward by a time shift interval of 2 s for the entire 5 min HRV segment. The ratios of Ln LF to Ln HF in these ultra-short-term segments and a single 5 min segment with a motion artifact were 0.89 and 1.06, respectively, while those in a motion artifact-free HRV segment were 0.75 and 0.93, respectively. This variation test for a short-term motion artifact and motion artifact-free HRV dataset was found to affect the SDNN (7.73 and 2.68), SD2 (11.44 and 4.42), TINN (40.33 and 9.92), and Ln HF (0.37 and 0.13) the most in terms of the standard deviation, respectively. Taken together, the mean HRV parameters of many ultra-short-term segments might play an important role in evaluating dynamic ANS activities within a short-term segment, avoiding the false conclusions made by the traditional HRV analysis.

Preliminary Comparison of Zero-Gravity Chair With Tilt Table in Relation to Heart Rate Variability Measurements.

Heart rate variability (HRV) measurements are performed using a tilt-table (TT) to diagnose dysfunctionality in the autonomic nervous system (ANS) and the cardiovascular system. To maintain homeostasis, the ANS adapts to body position changes through alterations in sympathetic and parasympathetic responses that can be quantified by extracting time-domain and frequency-domain parameters from the heart rate signal. When body position is changed from supine to erect, a healthy subject’s response also shows changes in ANS activity. However, TT can be unsafe or uncomfortable for elderly or overweight subjects. Furthermore, it may induce anxiety which alters the HRV measurements. This study proposes an alternative strategy to replace the TT with a zero-gravity chair (ZGC). The statistical analysis between HRV parameters from the TT and the ZGC shows that ZGC can be a feasible alternative to TT. Therefore, ZGC can be used as a more convenient, secure, stable and safer option to the traditional HRV analysis with TT.

Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics.

Background: Short-term heart rate variability (HRV) is most commonly attributed to physiologic vagal tone modulation. However, with aging and cardiovascular disease, the emergence of high short-term HRV, consistent with the breakdown of the neuroautonomic-electrophysiologic control system, may confound traditional HRV analysis. An apparent dynamical signature of such anomalous short-term HRV is frequent changes in heart rate acceleration sign, defined here as heart rate fragmentation.Objective: The aims were to: (1) introduce a set of metrics designed to probe the degree of sinus rhythm fragmentation; (2) test the hypothesis that the degree of fragmentation of heartbeat time series increases with the participants' age in a group of healthy subjects; (3) test the hypothesis that the heartbeat time series from patients with advanced coronary artery disease (CAD) are more fragmented than those from healthy subjects; and (4) compare the performance of the new fragmentation metrics with standard time and frequency domain measures of short-term HRV.Methods: We analyzed annotated, open-access Holter recordings (University of Rochester Holter Warehouse) from healthy subjects and patients with CAD using these newly introduced metrics of heart rate fragmentation, as well as standard time and frequency domain indices of short-term HRV, detrended fluctuation analysis and sample entropy.Results: The degree of fragmentation of cardiac interbeat interval time series increased significantly as a function of age in the healthy population as well as in patients with CAD. Fragmentation was higher for the patients with CAD than the healthy subjects. Heart rate fragmentation metrics outperformed traditional short-term HRV indices, as well as two widely used nonlinear measures, sample entropy and detrended fluctuation analysis short-term exponent, in distinguishing healthy subjects and patients with CAD. The same level of discrimination was obtained from the analysis of normal-to-normal sinus (NN) and cardiac interbeat interval (RR) time series.Conclusion: The fragmentation framework and accompanying metrics introduced here constitute a new way of assessing short-term HRV under free-running conditions, one which appears to overcome salient limitations of traditional HRV analysis. Fragmentation of sinus rhythm cadence may provide new dynamical biomarkers for probing the integrity of the neuroautonomic-electrophysiologic network controlling the heartbeat in health and disease.

QT variability improves risk stratification in patients with dilated cardiomyopathy

Recently it could be demonstrated that systolic and diastolic blood pressure variability (BPV) as well as segmented Poincare plot analysis (SPPA) contribute to risk stratification in patients suffering from dilated cardiomyopathy (DCM). The aim of this study was to improve the risk stratification applying a multivariate technique including QT variability (QTV). We enrolled and significantly separated 56 low risk and 13 high risk DCM patients by nearly all applied BPV and QTV methods, but not with traditional heart rate variability analysis. The optimum set of two indices calculating the multivariate discriminate analysis (DA) included one BPV index calculated by symbolic dynamics method (DBPShannon) and one index calculated from QTV (QTVlog) achieving an area under the receiver operating characteristics curve (AUC) of 92%, sensitivity of 92.3% and specificity of 89.3%. Performing only electrocardiogram analysis, the optimum multivariate approach including indices from segmented Poincaré plot analysis and QTV still achieved a remarkable AUC of 88.3%. Increasing the number of indices for multivariate DA up to three, we achieved an AUC of 95.7%, sensitivity of 100% and specificity of 85.7% including one clinical, one BPV and one QTV index. Summarizing, we identified DCM patients with an increased risk of sudden cardiac death applying QTV analysis in a multivariate approach.

A novel quantitative method for diabetic cardiac autonomic neuropathy assessment in type 1 diabetic mice.

In this work, we used a sensitive and noninvasive computational method to assess diabetic cardiovascular autonomic neuropathy (DCAN) from pulse oximeter (photoplethysmographic; PPG) recordings from mice. The method, which could be easily applied to humans, is based on principal dynamic mode (PDM) analysis of heart rate variability (HRV). Unlike the power spectral density, PDM has been shown to be able to separately identify the activities of the parasympathetic and sympathetic nervous systems without pharmacological intervention. HRV parameters were measured by processing PPG signals from conscious 1.5- to 5-month-old C57/BL6 control mice and in Akita mice, a model of insulin-dependent type 1 diabetes, and compared with the gold-standard Western blot and immunohistochemical analyses. The PDM results indicate significant cardiac autonomic impairment in the diabetic mice in comparison to the controls. When tail-cuff PPG recordings were collected and analyzed starting from 1.5 months of age in both C57/Bl6 controls and Akita mice, onset of DCAN was seen at 3 months in the Akita mice, which persisted up to the termination of the recording at 5 months. Western blot and immunohistochemical analyses also showed a reduction in nerve density in Akita mice at 3 and 4 months as compared to the control mice, thus, corroborating our PDM data analysis of HRV records. Western blot analysis of autonomic nerve proteins corroborated the PPG-based HRV analysis via the PDM approach. In contrast, traditional HRV analysis (based on either the power spectral density or time-domain measures) failed to detect the nerve rarefaction.

Removal of Respiratory Influences From Heart Rate Variability in Stress Monitoring

This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of monitoring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate monitors and respiratory sensors was constructed. Subtraction of respiratory driven fluctuations in heart rate leads to a residual signal where the effects of mental stress become more salient. We experimentally validated the effectiveness of this method on a binary discrimination problem with two conditions: mental stress of subjects performing cognitive tasks and a relaxation condition. In the process, we also propose a normalization method that can be used to compensate for ventilation differences between paced and spontaneous breathing. Our results suggest that, by separating respiration influences, the residual HRV has more discrimination power than traditional HRV analysis for the purpose of monitoring mental stress/load.

Changes of autonomic information flow due to idiopathic dilated cardiomyopathy

Risk stratification of patients with idiopathic dilated cardiomyopathy (IDC) is an epidemiologically relevant question. But the results based on conventional heart rate variability (HRV) analysis are still unsatisfactory. The adjustments within the cardiovascular system incorporate nonlinear and complex mechanisms of information exchange which may have additional prognostic value. It is an objective of the present work to evaluate the prognostic value of autonomic information flow (AIF) measures in IDC patients compared to conventional HRV measures in a first explorative study. Holter recordings of 32 patients with idiopathic dilated cardiomyopathy (IDC) and 12 normal subjects (NRM) were analyzed. The IDC patients consisted of two groups: 10 high risk (HR) patients, after aborted sudden cardiac death (SCD); 22 low risk (LR) patients, without SCD. Sensitivity, specificity, positive predictive value, negative predictive value and ROC characteristics of a comprehensive set of AIF measures, organized according to the conventional HRV standards, and conventional HRV measures were investigated. The significant risk predictors were evaluated by Spearman's rank correlation. While the only traditional HRV measure discriminating IDC patients from NRM was ln(LF) most of the AIF measures had a discriminatory value. Concerning the prognosis of the IDC patients by conventional HRV we found that SDNN and all frequency band measures (lnHF, lnLF, lnVLF) significantly discriminated HR from LR. Among the AIF measures the time shift related peak decay (PDdHF) reflecting the HF band information flow had a prognostic value. PDdHF was identified as a promising candidate which might improve the predictive value of traditional HRV analysis, predominantly represented by SDNN. A subsequent comprehensive clinical study is necessary to validate this hypothesis.

New method for the assessment of neonatal respiratory sinus arrhythmia

Respiratory sinus arrhythmia (RSA) is sometimes hard to identify in neonates, in particular when the respiratory rate approaches half the mean heart rate. It is shown that the analysis of the cardiac event series may reveal RSA in cases where traditional heart rate variability analysis methods fail. A signal analysis approach to event series processing is reviewed. The validation of the spectral analysis of event series is illustrated with the help of simulation results. In accordance with this approach, the event series spectrum of a neonate shows a RSA component at 0·9 Hz, which was in agreement with the frequency of the simultaneously measured respiration. The possible implications for research into the causes of sudden infant death syndrome (SIDS) are discussed.