Conventional Heart Rate Variability Research Articles

Using the Hilbert Transform to Evaluate the Effects of Functional Foods on Autonomic Nervous System Activity: A Comparison with the Fast Fourier Transform.

Evaluating the autonomic nervous system (ANS) via heart rate variability (HRV) to investigate the effects of food on human health has attracted attention. However, using a conventional HRV analysis via the fast Fourier transform (FFT), it is difficult to remove artifacts such as body movements and/or abnormal physiological responses (unexpected events) from the HRV analysis results. In this study, an analysis combining bandpass filters and the Hilbert transform was applied to HRV data on functional food intake to compare with FFT analysis. HRV data were obtained from six males by recording electrocardiograms on functional food, γ-aminobutyric acid, intake. HRV indices were calculated by both analysis. In the Hilbert analysis, all HRV indices were obtained for the same number of sampling points as the HRV data. The standard errors of all HRV indices tended to be smaller in the Hilbert analysis than in the FFT analysis. In conclusion, the Hilbert analysis was more suitable than FFT analysis for evaluating ANS via HRV on functional foods intake.

Recovery of cardiac electrophysiological alterations by heart rate complexity based on multiscale entropy following liver transplantation

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled. HRV parameters using conventional HRV analysis and HRC analysis were obtained at the following time points: (1) 1 day before surgery, (2) postoperative day (POD) 7, (3) POD 14, (4) POD 90, and (5) POD 180. Preoperatively, patients with MELD score ≥ 25 had significantly lower HRC compared to patients with lower MELD scores. This difference in HRC disappeared by POD 7 following liver transplantation and subsequent analyses at POD 90 and 180 continued to show no significant difference. Our results indicated a significant negative correlation between HRC based on MSE analysis and liver disease severity preoperatively, which may be more sensitive than conventional linear HRV analysis. HRC in patients with MELD score ≧ 25 improved over time and became comparable to those with MELD < 25 as early as in 7 days.

Indices Of Heart Rate Variability Are Not Associated With Obesity In Patients 30-60 Years Of Age Without Chronic Noncommunicable Diseases

Objective –– to compare heart rate variability (HRV) in patients aged 30-60 years without chronic noncommunicable diseases (CNCDs) with and without obesity. Methods –– The groups of obese (n=43) and nonobese (n=28) patients without CNCDs ranged 30 through 60 years of age. We assessed the conventional HRV indices according to the clinical guidelines, as well as the synchronization index (S) for low-frequency (LF) oscillations evaluated from HRV and photoplethysmogram (PPG). Results –– No statistically significant differences in HRV indices and S index were detected between the study groups. Conclusion –– HRV indices and synchronization of LF oscillations detected from HRV and PPG were not significantly associated with obesity in patients aged 30-60 years without CNCDs.

Abstract 16676: Risk Assesment of Sudden Cardiac Death With RR Interval Fluctuations

Introduction: Changes in conventional measures for short-term heart rate variability (HRV) have been associated with heart failure, coronary artery disease and even sudden cardiac death (SCD). However, the predictive value of HRV in clinical settings has been limited. Secondly, the current HRV methods have not discriminated SCDs from other causes of death, which would be relevant for proper prevention and treatment. In this study we utilize novel time series analysis methods for RR intervals in ultra-short-term measurements to distinguish SCD from other causes of death. Methods: We apply second-order detrended fluctuation analysis (DFA) with maximally overlapping windows to assess the correlations in RR intervals as a function of the scale, i.e., the range of consecutive RR intervals in one-minute resting ECG. The analysis is based on the baseline ECG recordings from the prospective FINCAVAS study of cardiac patients with a median follow-up time of 8.3 years, during which 378 deaths were recorded including 76 SCDs in the total population of 2794 subjects. Results: Our DFA method can discriminate between incident SCD cases and patients dying to other causes. The discrimination is maximized on scale 9, with an univariate hazard ratio of 0.71 and 95% confidence intervals (CI) (0.55-0.91). Excluding other cardiac deaths from the control group we are left with 279 deaths resulting to univariate hazard ratio of 0.62 (0.47-0.82 CI) on scale 8. Conclusions: The ability to distinguish SCD from other causes of death shows that the scale-dependent DFA method is a superior alternative to the conventional HRV methods. The utilization of one-minute RR interval measurements for risk assessment provides new non-invasive tools for clinical inspections, as well as promising prospects to health monitoring with wearable devices.

A Review of Methods and Applications for a Heart Rate Variability Analysis

Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease.

Heart rate variability (HRV) measures provide valuable insights into physiology; however, gaps remain in understanding circadian patterns in heart rate dynamics. We aimed to explore day-night differences in heart rate dynamics in patients with chronic cardiopulmonary disease compared with healthy controls. Using 24-hour heart rate data from patients with chronic obstructive pulmonary disease (COPD) and/or heart failure (n = 16) and healthy adult controls (older group: ≥50 years, n = 42; younger group: 20-49 years, n = 136), we compared day-night differences in conventional time and frequency domain HRV indices and a multiscale-entropy-based complexity index (CI1-20) of HRV among the 3 groups. Twenty-four-hour HRV showed significant day-night differences (marked with "△") among younger healthy (mean age: 34.5 years), older healthy (mean age: 61.6 years), and cardiopulmonary patients (mean age: 68.4 years), including change in percentage of adjacent intervals that differ > 50 ms (△pNN50), high frequency (△HF), normalized low frequency (△nLF), ratio (△LF/HF), and △CI1-20. Among these, △LF/HF (2.13 ± 2.35 vs 1.1 ± 2.47 vs -0.35 ± 1.25; P < .001) and △CI1-20 (0.15 ± 0.24 vs 0.02 ± 0.28 vs -0.21 ± 0.27; P < .001) were significant in each pairwise comparison following analysis of variance tests. Average CI1-20 was highest in younger healthy individuals and lowest in cardiopulmonary patients (1.37 ± 0.12 vs 1.01 ± 0.27; P < .001). Younger healthy patients showed a heart rate complexity dipping pattern (night < day), older healthy patients showed nondipping, and cardiopulmonary patients showed reverse dipping (night > day). As measures of 24-hour variability, traditional and complexity-based metrics of HRV exhibit large day-night differences in healthy individuals; these differences are blunted, or even reversed, in individuals with cardiopulmonary pathology. Measures of diurnal dynamics may be useful indices of reduced adaptive capacity in patients with cardiopulmonary conditions. Ma Y, Chang M-C, Litrownik D, Wayne PM, Yeh GY. Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease. J Clin Sleep Med. 2023;19(5):873-882.

Screening of obstructive sleep apnea syndrome by the deep breathing technique.

Obstructive sleep apnea syndrome (OSAS) is associated with alterations in heart rate variability (HRV) in relation to chronic autonomic dysfunction. We tested the ability of the deep breathing technique-a simple way to evaluate HRV-to identify patients with OSAS. Consecutive patients referred for suspected OSAS (without obesity, diabetes, and heart diseases) were included. They underwent a measure of HRV at rest and of heart rate oscillations during expiration vs inspiration (DeltaHRDB) when breathing deeply at the resonant frequency of 6 cycles per minute (deep breathing technique) while sitting awake, followed by a nighttime polysomnography. We measured DeltaHRDB and performed temporal and spectral HRV analysis. Of 31 included participants (77% male), 14 had mild to moderate OSAS (apnea-hypopnea index median [IQR]: 18 [12]) and 17 had no OSAS. The conventional HRV analysis did not reveal any difference between the groups with vs without OSAS. However, the DeltaHRDB was lower in those with than without OSAS. Lower DeltaHRDB correlated with higher apnea-hypopnea index, arousal index, and desaturation degree. A DeltaHRDB below 11 beats per minute (bpm) predicted OSAS with a sensitivity of 100% and specificity of 86%. The deep breathing technique accurately identifies a reduction in cardiac changes in patients with mild to moderate OSAS. It could be used as a simple screening tool to select patients for polysomnography. Onanga M, Joanny S, Rivals I, etal. Screening of obstructive sleep apnea syndrome by the deep breathing technique. J Clin Sleep Med. 2023;19(2):293-302.

A Novel Rapid Assessment of Mental Stress by Using PPG Signals Based on Deep Learning

People’s daily life is easily affected by mental stress, which can lead to mental illness in the long term. The current mental stress detection process is cumbersome, and the development of rapid assessment methods will make a great contribution to medical care. In view of this, this study used a pulse oximeter to obtain noninvasive photoplethysmography (PPG) signals, the measurement information was analyzed using heart rate variability (HRV), and the Poincaré plot of the heartbeat cycle was the output. Poincaré maps are used as input to deep learning (DL) to perform conditional prediction of mental stress. Finally, the results of conventional HRV and DL are compared. From the experimental results, the classification of the feature of the PPG signal (Poincaré plot) by the model is a meaningful and good result.

Detection and categorization of severe cardiac disorders based solely on heart period measurements

Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely by measuring a series of heart periods. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a metric of local variation was more efficient than conventional HRV metrics for alerting cardiac disorders, and furthermore, that a newly introduced metric of local-global variation resulted in superior capacity for discriminating between premature contraction and atrial fibrillation. Even with a 1-minute recording of heart periods, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update.

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

The Indices of Instantaneous Pulse Rate Variability Are Indicators for Daily Life Quality Assessment in Patients with COPD.

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory illness. Questionnaires such as modified Medical Research Council (mMRC) dyspnea scale and COPD assessment test (CAT) are useful for COPD condition and life quality assessment. These questionnaires reflect how respiratory disorder affects daily life. Breathing and autonomic nervous system (ANS) usually regulate each other. Few studies discussed the ANS activity and daily life quality in patients with COPD. Therefore, this study aimed to find the relationship between daily life quality assessed by mMRC or CAT and ANS assessed by a novel method, instantaneous pulse rate variability (iPRV), a method indicating not only the ANS activity but also the peripheral response. The result showed that the change in mMRC and the change in low frequency power to high frequency power ratio, which usually represents the sympathetic activity in conventional heart rate variability analysis, had significant correlation (r = 0.63; p < 0.05). The change in CAT and the change in high frequency power (regulated by vagal nervous and respiratory system) or very high frequency power (new frequency band can be indicated in iPRV spectrum) had significant negative correlation (r = −0.64 and −0.55, respectively; p < 0.05 for both). This study showed the change in iPRV indices when the condition of COPD was improvement or exacerbation. This study presents a possible way to show how cardiovascular activity affects daily life quality in patients with COPD. Increase in LF or decrease in HF and VHF would cause poorer quality of daily life in patients with COPD. The result can also be a reference for patients with COPD to choose the breathing type to adjust rehabilitation and therapy program for ANS regulation to indicate or improve their daily life quality.

Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications.

In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials.

Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System.

Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

AbstractPilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well‐known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in‐flight detection remains under‐investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying‐relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance‐Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight‐relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning‐based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

Observations of Autonomic Variability Following Central Neuromodulation for Chronic Neuropathic Pain in Spinal Cord Injury

Spinal cord injury (SCI) persons with chronic neuropathic pain (NP) demonstrate maladaptive autonomic profiles compared to SCI counterparts without NP (SCI - NP) or able-bodied (AB) controls. These aberrations may be secondary to maladaptive neuroplasticity in the shared circuitry of the pain neuromatrix-central autonomic network interface (PNM-CAN). In this study, we explored the proposed PNM-CAN mechanism in SCI + NP and AB cohorts following centrally-directed neuromodulation to assess if the PNM and CAN are capable of being differentially modulated. Central neuromodulation was administered via breathing-controlled electrical stimulation (BreEStim), previously evidenced to operate at the PNM. To quantify CAN activity, conventional heart rate variability (HRV) recordings were used to gather time and frequency domain parameters of autonomic modulation. SCI + NP (n = 10) and AB (n = 13) cohorts received null and active BreEStim randomly in crossover fashion. HRV data were gathered pretest and 30 minutes posttest. Pain modulation was quantified at both time-points by visual analog scale (VAS) for SCI + NP persons and electrical detection and pain threshold levels (EDT, EPT) for AB persons. Following active BreEStim only, SCI + NP persons demonstrated increased parasympathetic tone (increased NN50, p = 0.03, and pNN50, p = 0.02, HRV parameters). This parasympathetic restoration was associated with analgesia (VAS reduction, p < 0.01). Similarly, AB persons demonstrated increased noxious tolerance (increased EPT, p = 0.03, with preserved EDL, p = 0.78) only following active BreEStim. However, this increased pain threshold was not associated with autonomic changes. Central modulation targeting the PNM produced autonomic changes in SCI + NP persons but not AB persons. These findings suggest that AB persons exhibit intact CAN mechanisms capable of compensating for PNM aberrations or simply that SCI + NP persons exhibit altered PNM-CAN machinery altogether. Our collective findings confirm the interconnectedness and maladaptive plasticity of PNM-CAN machinery in SCI + NP persons and suggest that the PNM and CAN circuitry can be differentially modulated.

Cross-Spectral Analysis of Electrocardiographic and Nostril Airflow Signals Identifies Two Respiratory Frequencies of Heart Rate Modulation

Respiration is known to be a significant modulator of heart rate, and the high-frequency component in the power spectrum of heart rate variability (HRV) is believed to be caused mainly by respiration. To investigate the effect of respiration on heart rate, cross-spectral analysis of electrocardiographic (ECG) and nostril airflow signals was performed in healthy subjects to find the common features of ECG and respiration. Forty-two healthy subjects were included in this study. The autospectra of respective ECG and nostril airflow signals and the cross-spectra of ECG and nostril airflow signals were obtained and compared with the corresponding conventional HRV measures. We found that there were two spectral peaks at around 0.03 Hz and 0.3 Hz in the autospectrum of nostril airflow and the cross-spectrum of ECG and nostril airflow. In addition, the cross-spectral normalized high-frequency power (nHFPcs) was significantly larger than that of conventional HRV, while the cross-spectral normalized very low-frequency power (nVLFPcs), normalized low-frequency power (nLFPcs), and low-/high-frequency power ratio (LHRcs) were significantly lower than those of the conventional HRV. The cross-spectral nLFPcs and LHRcs had positive correlations with their corresponding HRV measures. We conclude that cross-spectral analysis of ECG and nostril airflow signals identifies two respiratory frequencies at around 0.03 Hz and below and around 0.3 Hz and can yield significantly enhanced nHFPcs and significantly suppressed nVLFPcs, as compared to their counterparts in conventional HRV. Both very low-frequency and high-frequency components of HRV are caused in part or mainly by respiration.

Heart Rate Turbulence Predicts Survival Independently From Severity of Liver Dysfunction in Patients With Cirrhosis.

BackgroundReduced heart rate variability (HRV) is an independent predictor of mortality in patients with cirrhosis. However, conventional HRV indices can only be interpreted in individuals with normal sinus rhythm. In patients with recurrent premature ventricular complexes (PVCs), the predictive capacity of conventional HRV indices is compromised. Heart Rate Turbulence (HRT) represents the biphasic change of the heart rate after PVCs. This study was aimed to define whether HRT parameters could predict mortality in cirrhotic patients.Materials and Methods24 h electrocardiogram recordings were collected from 40 cirrhotic patients. Turbulence Onset was calculated as HRT indices. The enrolled patients were followed up for 12 months after the recruitment in relation to survival and/or transplantation.ResultsDuring the follow-up period, 21 patients (52.5%) survived, 12 patients (30%) died and 7 patients (17.5%) had liver transplantation. Turbulence Onset was found to be strongly linked with mortality on Cox regression (Hazard ratio = 1.351, p < 0.05). Moreover, Turbulence Onset predicted mortality independently of MELD and Child-Pugh’s Score.ConclusionThis study provides further evidence of autonomic dysfunction in cirrhosis and suggests that HRT is reliable alternative to HRV in patients with PVCs.

Heart rhythm complexity as predictors for the prognosis of end-stage renal disease patients undergoing hemodialysis

BackgroundHeart rhythm complexity, a measure of heart rate dynamics and a risk predictor in various clinical diseases, has not been systematically studied in patients with end-stage renal disease. The aim of this study is to investigate the heart rhythm complexity and its prognostic value for mortality in end-stage renal disease patients undergoing hemodialysis.MethodsTo assess heart rhythm complexity and conventional heart rate variability measures, 4-h continuous electrocardiography for a retrospective cohort of 202 ostensibly healthy control subjects and 51 hemodialysis patients with end-stage renal disease were analyzed. Heart rhythm complexity was quantified by the complexity index from the measurement of the multiscale entropy profile.ResultsDuring a follow-up of 13 months, 8 people died in the patient group. Values of either traditional heart rate variability measurements or complexity indices were found significantly lower in patients than those in healthy controls. In addition, the complexity indices (Area 1–5, Area 6–15 and Area 6–20) in the mortality group were significantly lower than those in the survival group, while there were no significant differences in traditional heart rate variability parameters between the two groups. In receiver operating characteristic curve analysis, Area 6–20 (AUC = 0.895, p < 0.001) showed the strongest predictive power between mortality and survival groups.ConclusionThe results suggest that heart rhythm complexity is impaired for patients with end-stage renal disease. Furthermore, the complexity index of heart rate variability quantified by multiscale entropy may be a powerful independent predictor of mortality in end-stage renal disease patients undergoing hemodialysis.

Non-REM Sleep Marker for Wearable Monitoring: Power Concentration of Respiratory Heart Rate Fluctuation

A variety of heart rate variability (HRV) indices have been reported to estimate sleep stages, but the associations are modest and lacking solid physiological basis. Non-REM (NREM) sleep is associated with increased regularity of respiratory frequency, which results in the concentration of high frequency (HF) HRV power into a narrow frequency range. Using this physiological feature, we developed a new HRV sleep index named Hsi to quantify the degree of HF power concentration. We analyzed 11,636 consecutive 5-min segments of electrocardiographic (ECG) signal of polysomnographic data in 141 subjects and calculated Hsi and conventional HRV indices for each segment. Hsi was greater during NREM (mean [SD], 75.1 [8.3]%) than wake (61.0 [10.3]%) and REM (62.0 [8.4]%) stages. Receiver-operating characteristic curve analysis revealed that Hsi discriminated NREM from wake and REM segments with an area under the curve of 0.86, which was greater than those of heart rate (0.642), peak HF power (0.75), low-to-high frequency ratio (0.77), and scaling exponent α (0.77). With a cutoff &gt;70%, Hsi detected NREM segments with 77% sensitivity, 80% specificity, and a Cohen’s kappa coefficient of 0.57. Hsi may provide an accurate NREM sleep maker for ECG and pulse wave signals obtained from wearable sensors.

Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department

BackgroundChest pain is one of the most common complaints among patients presenting to the emergency department (ED). Causes of chest pain can be benign or life threatening, making accurate risk stratification a critical issue in the ED. In addition to the use of established clinical scores, prior studies have attempted to create predictive models with heart rate variability (HRV). In this study, we proposed heart rate n-variability (HRnV), an alternative representation of beat-to-beat variation in electrocardiogram (ECG), and investigated its association with major adverse cardiac events (MACE) in ED patients with chest pain.MethodsWe conducted a retrospective analysis of data collected from the ED of a tertiary hospital in Singapore between September 2010 and July 2015. Patients > 20 years old who presented to the ED with chief complaint of chest pain were conveniently recruited. Five to six-minute single-lead ECGs, demographics, medical history, troponin, and other required variables were collected. We developed the HRnV-Calc software to calculate HRnV parameters. The primary outcome was 30-day MACE, which included all-cause death, acute myocardial infarction, and revascularization. Univariable and multivariable logistic regression analyses were conducted to investigate the association between individual risk factors and the outcome. Receiver operating characteristic (ROC) analysis was performed to compare the HRnV model (based on leave-one-out cross-validation) against other clinical scores in predicting 30-day MACE.ResultsA total of 795 patients were included in the analysis, of which 247 (31%) had MACE within 30 days. The MACE group was older, with a higher proportion being male patients. Twenty-one conventional HRV and 115 HRnV parameters were calculated. In univariable analysis, eleven HRV and 48 HRnV parameters were significantly associated with 30-day MACE. The multivariable stepwise logistic regression identified 16 predictors that were strongly associated with MACE outcome; these predictors consisted of one HRV, seven HRnV parameters, troponin, ST segment changes, and several other factors. The HRnV model outperformed several clinical scores in the ROC analysis.ConclusionsThe novel HRnV representation demonstrated its value of augmenting HRV and traditional risk factors in designing a robust risk stratification tool for patients with chest pain in the ED.