On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods

Glyceryl trinitrate-induced blood pressure variability decrease during head-up tilt test predicts vasovagal response

PURPOSE

Glyceryl trinitrate (GTN) provoked cardioinhibitory syncope during the head-up tilt test is preceded by a period of disrupted blood pressure variability (BPV). Endogenous nitric oxide (NO) attenuates BPV independently of blood pressure (BP). We hypothesized that exogenous NO donor GTN might decrease BPV during the presyncope period. A decrease in BPV may predict the tilt outcome.

METHODS

We analyzed 29 tilt test recordings of subjects with GTN-induced cardioinhibitory syncope and 30 recordings of negative subjects. A recursive autoregressive model of BPV after GTN was performed; powers of the respiratory (0.15-0.45 Hz) and nonrespiratory frequency (0.01-0.15 Hz) bands were calculated for each of the 20 normalized time periods. The post-GTN relative changes in heart rate, BP, and BPV were calculated.

RESULTS

In the syncope group, spectral power of nonrespiratory frequency systolic and diastolic BPV progressively felt for 30% after GTN application and stabilized after 180 s. BP started to fall 240 s after the GTN application. Decrease in nonrespiratory frequency power of diastolic BPV 20 s after GTN administration predicted cardioinhibitory syncope (area under the curve 0.811; 77% sensitivity; 70% specificity; cutoff value > 7%).

CONCLUSION

GTN application during the tilt test attenuates systolic and diastolic nonrespiratory frequency BPV during the presyncope period, independent of BP. A decrease in nonrespiratory frequency diastolic BPV 20 s after GTN application predicts cardioinhibitory syncope with good sensitivity and moderate specificity.

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.

The effect of passive lower limb training on heart rate asymmetry

OBJECTIVE

Heart rate asymmetry (HRA) is an approach for quantitatively assessing the uneven distribution of heart rate accelerations and decelerations for sinus rhythm. We aimed to investigate whether automatic regulation led to HRA alternation during passive lower limb training.

METHODS

Thirty healthy participants were recruited in this study. The protocol included a baseline (Pre-E) and three passive lower limb training trials (E1, E2 and E3) with a randomized order. Several variance-based HRA variables were established. Heart rate variability (HRV) parameters, i.e., mean RR, SDNN, RMSSD, LF (n.u.), HF (n.u.) and VLF (ms2), and HRA variables, i.e., SD1a, SD1d, SD2a, SD2d, SDNNa and SDNNd, were calculated by using 5-min RR time series, as well as the normalized HRA variables, i.e., C1a, C1d, C2a, C2d, Ca and Cd.

RESULTS

Our results showed that the performance of HRA was distinguished. The normalized HRA was observed with significant changes in E1, E2 and E3 compared to Pre -E. Moreover, parts of non-normalized HRA variables correlated with HRV parameters, which indicated that HRA might benefit in assessing cardiovascular modulation in passive lower limb training.

CONCLUSIONS

In summary, this study suggested that passive training led to significant HRA alternation and the application of HRA gave us the possibility for autonomic assessment.

A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10−4 and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport.

A method to quantify autonomic nervous system function in healthy, able-bodied individuals

BACKGROUND

The autonomic nervous system (ANS) maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches. ANS function is altered in common diffuse and focal conditions and heralds the beginning of environmental and disease stresses. Reliable, sensitive, and quantitative biomarkers, first defined in healthy participants, could discriminate among clinically useful changes in ANS function. This framework combines controlled autonomic testing with feature extraction during physiological responses.

METHODS

Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population.

RESULTS

A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions.

CONCLUSIONS

These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation.

TRIAL REGISTRATION

This study registered with Clinicaltrials.gov , identifier NCT04100486 . Registered September 24, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04100486 .

Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users' understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.

Exponential model for analysis of heart rate responses and autonomic cardiac modulation during different intensities of physical exercise

The aim of this study was to compare the heart rate (HR) dynamics and variability before and after high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) protocols with workloads based on treadmill workload at which maximal oxygen uptake was achieved ( WL V ˙ O 2 max ) . Ten participants performed cardiopulmonary exercise testing (CPET) to obtain oxygen uptake ( WL V ˙ O 2 max ) . All training protocols were performed on a treadmill, with 0% grade, and had similar total distance. The MICT was composed by 21 min at 70% of WL V ˙ O 2 max . The first HIIT protocol (HIIT-30 : 30) was composed by 29 repetitions of 30 s at 100% of s V ˙ O 2 max and the second HIIT protocol (HIIT-4 : 3) was composed by three repetitions of 4 min at 90% of WL V ˙ O 2 max . Before, during and after each training protocol, HR dynamics and variability (HRV) were analysed by standard kinetics and linear (time and frequency domains). The repeated measures analysis of variance indicated that the HR dynamics, which characterizes the speed of HR during the rest to exercise transition, was statistically (p < 0.05) slower during MICT in comparison to both HIIT protocols. The HRV analysis, which characterizes the cardiac autonomic modulation during the exercise recovery, was statistically higher in HIIT-4 : 3 in comparison to MICT and HIIT-30 : 30 protocols (p < 0.005 and p = 0.012, respectively), suggesting that the HIIT-4 : 3 induced higher sympathetic and lower parasympathetic modulation during exercise in comparison to the other training protocols. In conclusion, HIIT-4 : 3 demonstrated post-exercise sympathetic hyperactivity and a higher HRpeak, while the HIIT-30 : 30 and MICT resulted in better HRV and HR in the exercise-recovery transition. The cardiac autonomic balance increased in HIIT-30 : 30 while HIIT-4 : 3 induced sympathetic hyperactivity and cardiac overload.

Spectral Analysis of Heart Rate Variability: Time Window Matters

Spectral analysis of heart rate variability (HRV) is a valuable tool for the assessment of cardiovascular autonomic function. Fast Fourier transform and autoregressive based spectral analysis are two most commonly used approaches for HRV analysis, while new techniques such as trigonometric regressive spectral (TRS) and wavelet transform have been developed. Short-term (on ECG of several minutes) and long-term (typically on ECG of 1–24 h) HRV analyses have different advantages and disadvantages. This article reviews the characteristics of spectral HRV studies using different lengths of time windows. Short-term HRV analysis is a convenient method for the estimation of autonomic status, and can track dynamic changes of cardiac autonomic function within minutes. Long-term HRV analysis is a stable tool for assessing autonomic function, describe the autonomic function change over hours or even longer time spans, and can reliably predict prognosis. The choice of appropriate time window is essential for research of autonomic function using spectral HRV analysis.

A new approach for analysis of heart rate variability and QT variability in long-term ECG recording

Background and purpose

With the emergence of long-term electrocardiogram (ECG) recordings that extend several days beyond the typical 24–48 h, the development of new tools to measure heart rate variability (HRV) and QT variability is needed to utilize the full potential of such extra-long-term ECG recordings.

Methods

In this report, we propose a new nonlinear time–frequency analysis approach, the concentration of frequency and time (ConceFT), to study the HRV QT variability from extra-long-term ECG recordings. This approach is a generalization of Short Time Fourier Transform and Continuous Wavelet Transform approaches.

Results

As proof of concept, we used 14-day ECG recordings to show that the ConceFT provides a sharpened and stabilized spectrogram by taking the phase information of the time series and the multitaper technique into account.

Conclusion

The ConceFT has the potential to provide a sharpened and stabilized spectrogram for the heart rate variability and QT variability in 14-day ECG recordings.

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0490-8) contains supplementary material, which is available to authorized users.

Time-varying assessment of heart rate variability parameters using respiratory information

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are estimated using smoothed extended Kalman filtering. Results for different synthetic data show that our proposed joint model outperforms the classical AR modeling in estimation of HRV parameters especially in the case of low respiration rate. In addition, the possibility of using pulse transit time (PTT) and the amplitude of photoplethysmogram (PPG_amp) as surrogates of the input respiratory signal has been investigated. To this end, electrocardiogram (ECG), PPG and respiration have been recorded from 21 healthy subjects (10 males and 11 females, mean age 27.5 ± 4.1) during normal and deep respiration. Results show that indeed PTT and PPG_amp offer good potential to be used as references for respiratory-related variations of HR, thus avoiding additional devices for recording respiration.

Modeling volatility in heat rate variability

Modeling Heart Rate Variability (HRV) data has become important for clinical applications and as a research tool. These data exhibit long memory and time-varying conditional variance (volatility). In HRV, volatility is traditionally estimated by recursive least squares combined with short memory AutoRegressive (AR) models. This work considers a parametric approach based on long memory Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with heteroscedastic errors. To model the heteroscedasticity nonlinear Generalized Autoregressive Conditionally Heteroscedastic (GARCH) and Exponential Generalized Autoregressive Conditionally Heteroscedastic (EGARCH) models are considered. The latter are necessary to model empirical characteristics of conditional volatility such as clustering and asymmetry in the response, usually called leverage in time series literature. The ARFIMA-EGARCH models are used to capture and remove long memory and characterize conditional volatility in 24 hour HRV recordings from the Noltisalis database.

Epileptic seizure onset detection based on EEG and ECG data fusion

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.

Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment

Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).

Sudden Cardiac Risk Stratification with Electrocardiographic Indices - A Review on Computational Processing, Technology Transfer, and Scientific Evidence

Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indices, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indices in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indices which are tackled from the aforementioned viewpoints, namely, heart rate turbulence (HRT), heart rate variability (HRV), and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

Modeling heart rate variability including the effect of sleep stages

We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.

Modulation of Frontoparietal Neurovascular Dynamics in Working Memory

Our perception of the world is represented in widespread, overlapping, and interactive neuronal networks of the cerebral cortex. A majority of physiological studies on the subject have focused on oscillatory synchrony as the binding mechanism for representation and transmission of neural information. Little is known, however, about the stability of that synchrony during prolonged cognitive operations that span more than just a few seconds. The present research, in primates, investigated the dynamic patterns of oscillatory synchrony by two complementary recording methods, surface field potentials (SFPs) and near-infrared spectroscopy (NIRS). The signals were first recorded during the resting state to examine intrinsic functional connectivity. The temporal modulation of coactivation was then examined on both signals during performance of working memory (WM) tasks with long delays (memory retention epochs). In both signals, the peristimulus period exhibited characteristic features in frontal and parietal regions. Examination of SFP signals over delays lasting tens of seconds, however, revealed alternations of synchronization and desynchronization. These alternations occurred within the same frequency bands observed in the peristimulus epoch, without a specific correspondence between any definite cognitive process (e.g., WM) and synchrony within a given frequency band. What emerged instead was a correlation between the degree of SFP signal fragmentation (in time, frequency, and brain space) and the complexity and efficiency of the task being performed. In other words, the incidence and extent of SFP transitions between synchronization and desynchronization-rather than the absolute degree of synchrony-augmented in correct task performance compared with incorrect performance or in a control task without WM demand. An opposite relationship was found in NIRS: increasing task complexity induced more uniform, rather than fragmented, NIRS coactivations. These findings indicate that the particular features of neural oscillations cannot be linearly mapped to cognitive functions. Rather, information and the cognitive operations performed on it are primarily reflected in their modulations over time. The increased complexity and fragmentation of electrical frequencies in WM may reflect the activation of hierarchically diverse cognits (cognitive networks) in that condition. Conversely, the homogeneity in coherence of NIRS responses may reflect the cumulative vascular reactions that accompany that neuroelectrical proliferation of frequencies and the longer time constant of the NIRS signal. These findings are directly relevant to the mechanisms mediating cognitive processes and to physiologically based interpretations of functional brain imaging.

Reduced Irregularity of Ventricular Response During Atrial Fibrillation and Long-term Outcome in Patients With Heart Failure

Reduced heart rate variability (HRV) is associated with poor outcome in patients with heart failure (HF). However, the data on predictive value of RR variability during atrial fibrillation (AF) are limited. Therefore, the aim of this study was to evaluate the association between ventricular response characteristics and long-term clinical outcome in the population of ambulatory patients with mild-to-moderate HF and AF at baseline. The study included 155 patients (mean age 69 ± 10 years) with AF at 20-minute Holter electrocardiographic (ECG) recordings at enrollment. HRV analysis included SDNN, rMSSD, and pNN50, whereas irregularity indexes included 2 nonlinear parameters: approximate entropy (ApEn) and Shannon entropy. After median 41 months of follow-up, 54 patients died, including 21 HF related and 16 sudden deaths. Patients with ApEn ≤1.68 (lower tertile) had 40% mortality versus 12% in others (p <0.001) at 2 years of follow-up. Only nonlinear HRV parameters (irregularity but not variability indexes) identified patients at higher risk during follow-up. Decreased ApEn ≤1.68 was an independent predictor of total mortality (hazard ratio [HR] 2.81, 95% confidence interval [CI] 1.61 to 4.89, p <0.001), sudden cardiac death (HR 3.83, 95% CI 1.31 to 11.25, p = 0.014), and HF death (HR 3.45, 95% CI 1.42 to 8.38, p = 0.006) in a multivariate Cox analysis. In conclusion, in a post hoc analysis of Muerte Subita en Insufficiencia Cardiaca study AF cohort, reduced irregularity of RR intervals during AF, likely caused by autonomic dysfunction, was an independent predictor of all-cause mortality and sudden death and HF progression in patients with mild-to-moderate HF, whereas traditional HRV indexes did not predict outcome.

Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.

Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal

Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.

Wavelet representation of the corneal pulse for detecting ocular dicrotism

Purpose

To develop a reliable and powerful method for detecting the ocular dicrotism from non-invasively acquired signals of corneal pulse without the knowledge of the underlying cardiopulmonary information present in signals of ocular blood pulse and the electrical heart activity.

Methods

Retrospective data from a study on glaucomatous and age-related changes in corneal pulsation [PLOS ONE 9(7),(2014):e102814] involving 261 subjects was used. Continuous wavelet representation of the signal derivative of the corneal pulse was considered with a complex Gaussian derivative function chosen as mother wavelet. Gray-level Co-occurrence Matrix has been applied to the image (heat-maps) of CWT to yield a set of parameters that can be used to devise the ocular dicrotic pulse detection schemes based on the Conditional Inference Tree and the Random Forest models. The detection scheme was first tested on synthetic signals resembling those of a dicrotic and a non-dicrotic ocular pulse before being used on all 261 real recordings.

Results

A detection scheme based on a single feature of the Continuous Wavelet Transform of the corneal pulse signal resulted in a low detection rate. Conglomeration of a set of features based on measures of texture (homogeneity, correlation, energy, and contrast) resulted in a high detection rate reaching 93%.

Conclusion

It is possible to reliably detect a dicrotic ocular pulse from the signals of corneal pulsation without the need of acquiring additional signals related to heart activity, which was the previous state-of-the-art. The proposed scheme can be applied to other non-stationary biomedical signals related to ocular dynamics.

Time-frequency analysis of heart rate variability during the cold pressor test using a time-varying autoregressive model

Heart rate variability is a useful clinical tool for autonomic function assessment and cardiovascular disease diagnosis. To investigate the dynamic changes of sympathetic and parasympathetic activities during the cold pressor test, we used a time-varying autoregressive model for the time-frequency analysis of heart rate variability in 101 healthy subjects. We found that there were two sympathetic peaks (or two parasympathetic valleys) when the abrupt changes of temperature (ACT) occurred at the beginning and the end of the cold stimulus and that the sympathetic and parasympathetic activities returned to normal in about the last 2 min of the cold stimulus. These findings suggested that the ACT rather than the low temperature was the major cause of the sympathetic excitation and parasympathetic withdrawal. We also found that the onsets of the sympathetic peaks were 4-26 s prior to the ACT and the returns to normal were 54-57 s after the ACT, which could be interpreted as the feedforward and adaptation of the autonomic regulation process in the human body, respectively. These results might be helpful for understanding the regulatory mechanisms of the autonomic system and its effects on the cardiovascular system.

Effects of stellate ganglion block on cardiovascular reaction and heart rate variability in elderly patients during anesthesia induction and endotracheal intubation

STUDY OBJECTIVE

To investigate the effects of stellate ganglion block (SGB) on cardiovascular response and heart rate (HR) variability in elderly patients during anesthesia induction and endotracheal intubation.

DESIGN

A randomized, double-blinded, and placebo-controlled study.

SETTING

University-affiliated teaching hospital.

PARTICIPANTS

Eighty elderly patients (American Society of Anesthesiologists grades I and II) receiving elective surgery during general anesthesia.

INTERVENTIONS

Right stellate ganglion injection (SGB) was performed in all patients using 10 mL of 1% lidocaine or normal saline.

MEASUREMENTS

Systolic blood pressure (BP), diastolic BP, HR, and calculated rate pressure product. HR variability at the following time points: conscious status before induction (T0); immediately before intubation (T1); immediately after intubation (T2); and 1, 3, and 5 minutes postintubation (T3, T4, and T5).

MAIN RESULTS

No significant differences in BP and HR were observed between the 2 groups. Rate pressure product values significantly increased in the control group compared with baseline and SGB group values. Low-frequency power (LF) and LF/high-frequency power (HF) significantly increased, and HF and normalized units of HF significantly decreased in the control group compared with baseline values. LF, normalized units of LF, and LF/HF in the SGB group significantly decreased compared with those of the control group.

CONCLUSION

SGB protects the myocardium and effectively suppresses stress responses during anesthesia induction and tracheal intubation in elderly patients.

Additive effect of simultaneously varying respiratory frequency and tidal volume on respiratory sinus arrhythmia

Our aims were to assess, in healthy young females and males, the effects of the linear joint variation of respiratory frequency (RF) and tidal volume (VT) on the logarithmic transformation of high-frequency power of RR intervals (lnHF). ECG and VT were recorded from 18 females and 20 males during three visually guided 30-s breathing maneuvers: linearly increasing RF (RFLI) at constant VT; linearly increasing VT (VTLI) followed by decreasing VT (VTLD) at fixed RF, and RFLI and VTLI-VTLD combined. VT of females was 20% smaller. Instantaneous RF and lnHF were computed from the time-frequency distributions of respiratory series and RR intervals. LnHF-RF and lnHF-VT relations were similar between genders. LnHF and RR intervals control-maneuver differences during combined maneuver were approximately equal to the sum of those of the independent maneuvers. LnHF-RFLI relation showed strong negative correlations in separated and combined conditions, with steeper slope in the latter (p < 0.001). LnHF-VTLI and lnHF-VTLD relations presented, in the independent maneuvers, three combinations of slopes of different sign, all with hysteresis, and in the combined maneuver, strong correlations with negative slope for VTLI and positive slope for VTLD, steeper (p < 0.001) and with greater hysteresis (p < 0.001) than the independent ones. LnHF responses to our fast, non-fatiguing and non-steady-state breathing maneuvers are: similar between genders; consistent attenuation due to RFLI, whether applied alone or combined; ambiguous and with hysteresis to independent VTLI-VTLD variations; systematic greater attenuation during RFLI combined with VTLI-VTLD, equal to the sum of the independent effects, indicating that there is no interference between them.

Supraventricular arrhythmias after thoracotomy: is there a role for autonomic imbalance?

Supraventricular arrhythmias are common rhythm disturbances following pulmonary surgery. The overall incidence varies between 3.2% and 30% in the literature, while atrial fibrillation is the most common form. These arrhythmias usually have an uneventful clinical course and revert to normal sinus rhythm, usually before patent's discharge from hospital. Their importance lies in the immediate hemodynamic consequences, the potential for systemic embolization and the consequent long-term need for prophylactic drug administration, and the increased cost of hospitalization. Their incidence is probably related to the magnitude of the performed operative procedure, occurring more frequently after pneumonectomy than after lobectomy. Investigators believe that surgical factors (irritation of the atria per se or on the ground of chronic inflammation of aged atria), direct injury to the anatomic structure of the autonomic nervous system in the thoracic cavity, and postthoracotomy pain may contribute independently or in association with each other to the development of these arrhythmias. This review discusses currently available information about the potential mechanisms and risk factors for these rhythm disturbances. The discussion is in particular focused on the role of postoperative pain and its relation to the autonomic imbalance, in an attempt to avoid or minimize discomfort with proper analgesia utilization.

Laser Acupuncture: Two Acupoints (Baihui, Neiguan) and Two Modalities of Laser (658 nm, 405 nm) Induce Different Effects in Neurovegetative Parameters

There are only few scientific publications dealing with the basic investigation of the effects of only one or two acupoints or comparing one single point with another single point, using different stimulation methods in the same persons. The aim of this needle-controlled, randomized crossover study was to investigate the neurovegetative parameters heart rate (HR) and heart rate variability (HRV) using two different acupoints, Baihui (GV20) and Neiguan (PC6), in separate sessions. We investigated 11 healthy volunteers (3 m, 8 f) with a mean age ± SD of 22.9 ± 2.8 years. The two acupoints were stimulated for 10 minutes each with manual needle acupuncture, red laser acupuncture (658 nm), and violet laser acupuncture (405 nm), in randomized order. Needle and red laser stimulation of the Baihui acupoint decreased HR significantly. Only violet laser stimulation at the Neiguan acupoint induced a significant increase of total HRV. Further studies using other neurovegetative parameters and more volunteers are necessary to confirm the preliminary results.

Analysis of heart rate variability and skin blood flow oscillations under deep controlled breathing

The effect of deep breathing controlled in both rate (0.25, 0.16, 0.1, 0.07, 0.05 and 0.03 Hz) and amplitude on the heart rate variability (HRV) and respiration-dependent oscillations of forearm/finger skin blood flow (SBF) has been studied in 29 young healthy volunteers. The influence of sympathovagal balance on the respiratory sinus arrhythmia (RSA) amplitude and respiratory SBF oscillations has been studied. The subjects with predominant parasympathetic tonus had statistically significant higher RSA amplitudes in the breathing rate region of 0.03-0.07 Hz than the subjects with predominant sympathetic tonus. In the finger-cushion zone, having a well-developed sympathetic vascular innervations, the amplitudes of respiratory SBF oscillations at breathing rates 0.05 and 0.07 Hz were higher in the group of subjects with predominant parasympathetic tonus. In the forearm skin, where the density of sympathetic innervations is low comparatively to that in the finger skin, no statistically significant differences in the amplitude of respiratory SBF oscillations were found concerning the two groups of subjects.

A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs

Nonlinear type system identification models coupled with white noise stimulation provide an experimentally convenient and quick way to investigate the often complex and nonlinear interactions between the mechanical and neural elements of reflex limb control systems. Previous steady state analysis has allowed the neurons in such systems to be categorised by their sensitivity to position, velocity or acceleration (dynamics) and has improved our understanding of network function. These neurons, however, are known to adapt their output amplitude or spike firing rate during repetitive stimulation and this transient response may be more important than the steady state response for reflex control. In the current study previously used system identification methods are developed and applied to investigate both steady state and transient dynamic and nonlinear changes in the neural circuit responsible for controlling reflex movements of the locust hind limbs. Through the use of a parsimonious model structure and Monte Carlo simulations we conclude that key system dynamics remain relatively unchanged during repetitive stimulation while output amplitude adaptation is occurring. Whilst some evidence of a significant change was found in parts of the systems nonlinear response, the effect was small and probably of little physiological relevance. Analysis using biologically more realistic stimulation reinforces this conclusion.

Sino-European Transcontinental Basic and Clinical High-Tech Acupuncture Studies-Part 4: "Fire of Life" Analysis of Heart Rate Variability during Acupuncture in Clinical Studies

This fourth part of a series of Sino-European high-tech acupuncture studies describes the first clinical transcontinental teleacupuncture measurements in two patients (cervical spine syndrome and tachycardia; both 27 years old) from the Beijing Hospital of Traditional Chinese Medicine affiliated to Capital Medical University, China. The electrocardiographic data were transferred to the Stronach Research Unit for Complementary and Integrative Laser Medicine and the TCM Research Center in Graz via conventional internet connections. Data analysis was performed in Graz using a new “Fire of Life” heart rate variability analysis. Analysis was performed without any technical problems in both subjects. Heart rate decreased significantly during acupuncture in the two patients from Beijing. At the same time, total HRV increased during acupuncture. The different influences of HRV (respiratory sinus arrhythmia, blood pressure waves, etc.) could be clearly documented using the new “Fire of Life” analysis.

Heart rate variability dynamics during low-dose propofol and dexmedetomidine anesthesia

Heart rate variability (HRV) has been observed to decrease during anesthesia, but changes in HRV during loss and recovery of consciousness have not been studied in detail. In this study, HRV dynamics during low-dose propofol (N = 10) and dexmedetomidine (N = 9) anesthesia were estimated by using time-varying methods. Standard time-domain and frequency-domain measures of HRV were included in the analysis. Frequency-domain parameters like low frequency (LF) and high frequency (HF) component powers were extracted from time-varying spectrum estimates obtained with a Kalman smoother algorithm. The Kalman smoother is a parametric spectrum estimation approach based on time-varying autoregressive (AR) modeling. Prior to loss of consciousness, an increase in HF component power indicating increase in vagal control of heart rate (HR) was observed for both anesthetics. The relative increase of vagal control over sympathetic control of HR was overall larger for dexmedetomidine which is in line with the known sympatholytic effect of this anesthetic. Even though the inter-individual variability in the HRV parameters was substantial, the results suggest the usefulness of HRV analysis in monitoring dexmedetomidine anesthesia.

Biomedical signal and image processing

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.

Signal processing of heart signals for the quantification of non-deterministic events

BACKGROUND

Heart signals represent an important way to evaluate cardiovascular function and often what is desired is to quantify the level of some signal of interest against the louder backdrop of the beating of the heart itself. An example of this type of application is the quantification of cavitation in mechanical heart valve patients.

METHODS

An algorithm is presented for the quantification of high-frequency, non-deterministic events such as cavitation from recorded signals. A closed-form mathematical analysis of the algorithm investigates its capabilities. The algorithm is implemented on real heart signals to investigate usability and implementation issues. Improvements are suggested to the base algorithm including aligning heart sounds, and the implementation of the Short-Time Fourier Transform to study the time evolution of the energy in the signal.

RESULTS

The improvements result in better heart beat alignment and better detection and measurement of the random events in the heart signals, so that they may provide a method to quantify nondeterministic events in heart signals. The use of the Short-Time Fourier Transform allows the examination of the random events in both time and frequency allowing for further investigation and interpretation of the signal.

CONCLUSIONS

The presented algorithm does allow for the quantification of nondeterministic events but proper care in signal acquisition and processing must be taken to obtain meaningful results.

Heart rate variability as a measure of cardiac autonomic function in anorexia nervosa: a review of the literature

OBJECTIVE

Patients with anorexia nervosa (AN) exhibit a wide range of autonomic system disturbances; these patients have also high mortality risk due to cardio-vascular complications. Origin and pathogenesis of such changes are not absolutely clear.

METHODS

Relevant publications were drawn from PUBMED using the keywords 'anorexia nervosa' AND 'autonomic'. Fifty two abstracts were identified and screened for papers that measured the state of autonomic regulation by means of analysis of heart rate variability either during 24-hour electrocardiography (ECG) monitoring or during a short-term laboratory test. Studies selected were analysed for the number of patients included, the presence and quality of control groups, gender, age and body mass index (BMI) of patients, type of AN as well as methods used to determine heart rate variability (HRV).

RESULTS

Twenty papers on HRV in patients with anorexia were identified and analysed, revealing three distinct positions regarding changes of autonomic nervous system (ANS) functions in patients with AN. The majority of papers identified parasympathetic/sympathetic imbalance with parasympathetic dominance and decreased sympathetic modulation; others could not replicate these findings, but instead described sympathetic dominance; finally a group of papers could not identify any autonomic differences in comparison to control samples. We conclude that in its current state of analysis HRV may not be suitable for routine assessment of ANS function in AN patients but rather remains a research tool.

Dynamic assessment of baroreflex control of heart rate during induction of propofol anesthesia using a point process method

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R-R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RR-->BP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.

On the reliability of frequency components in systolic arterial pressure in patients with atrial fibrillation

Atrial fibrillation (AF) is characterized by desynchronization of atrial electrical activity causing a consequent irregular ventricular response. In AF, the beat-to-beat variation of blood pressure is increased because of variations in filling time and contractility. However, only a few studies have analyzed short-term blood pressure variations in AF, and we have recently observed a harmonic low-frequency (LF) component in systolic arterial pressure (SAP) during AF. Aim of the present study is to propose a method to verify the reliability of the spectral component found in SAP series, based on the position of the poles of the autoregressive spectral decomposition in the z-plane. In particular, 1,000 random permutations of the series allowed the definition of an area in the z-plane where poles from random process are likely to occur. Poles lying outside this area are considered as reliable oscillations. We tested the method on 53 recordings obtained at rest from patients with persistent AF. LF component was found in, respectively, 51 and 43 recordings in SAP and RR series. High-frequency (HF) component was found in all the recordings for both SAP and RR series. Using the proposed test, the percentage of reliable components in LF and HF bands was 80 and 38 in SAP series, and 20 and 18 in RR series. We concluded that, at variance with RR ones, SAP LF components are likely to represent true physiological oscillations.