A framework for modeling performers' beat-to-beat heart intervals using music features and Interpretation Maps

Quantification of the acute effect of a low dose of red wine by nonlinear measures of RR and QT interval series in healthy subjects

BackgroundUndetected arrhythmic beats seriously affect the power spectrum of the heart rate variability (HRV). Therefore, the series of RR intervals are normally carefully edited before HRV is analysed, but this is a time consuming procedure when 24-hours recordings are analysed. Alternatively, different methods can be used for automatic removal of arrhythmic beats and artefacts. This study compared common frequency domain indices of HRV when determined from manually edited and automatically filtered RR intervals.Methods and ResultsTwenty-four hours Holter recordings were available from 140 healthy subjects of age 1-75 years. An experienced technician carefully edited all recordings. Automatic filtering was performed using a recursive procedure where RR intervals were removed if they differed from the mean of the surrounding RR intervals with more than a predetermined limit (ranging from 10% to 50%). The filtering algorithm was evaluated by replacing 1% of the beats with synthesised ectopic beats. Power spectral analysis was performed before and after filtering of both the original edited data and the noisy data set. The results from the analysis using the noisy data were used to define an age-based filtering threshold. The age-based filtration was evaluated with completely unedited data, generated by removing all annotations from the series of RR intervals, and then comparing the resulting HRV indices with those obtained using edited data. The results showed equivalent results after age-based filtration of both the edited and unedited data sets, where the differences in HRV indices obtained by different preprocessing methods were small compared to the mean values within each age group.ConclusionsThe study showed that it might not be necessary to perform the time-consuming careful editing of all detected heartbeats before HRV is analysed in Holter recordings.In most subjects, it is sufficient to perform the regular editing needed for valid arrhythmia analyses, and then remove undetected ectopic beats and artefacts by age-based filtration of the series of RR intervals, particularly in subjects older than 30 years.

Cerebral autoregulatory changes in hypertensive states assessed by beat to beat cerebral blood velocity changes

Electrocardiographic RR intervals fluctuate cyclically, modulated by ventilation, baroreflexes, and other genetic and environmental factors that are mediated through the autonomic nervous system. Short term electrocardiographic recordings (5 to 15 minutes), made under controlled conditions, e.g., lying supine or standing or tilted upright can elucidate physiologic, pharmacologic, or pathologic changes in autonomic nervous system function. Long-term, usually 24-hour recordings, can be used to assess autonomic nervous responses during normal daily activities in health, disease, and in response to therapeutic interventions, e.g., exercise or drugs. RR interval variability is useful for assessing risk of cardiovascular death or arrhythmic events, especially when combined with other tests, e.g., left ventricular ejection fraction or ventricular arrhythmias.

Detection and significance of a discrete very low frequency rhythm in RR interval variability in chronic congestive heart failure

Prognostic significance of circadian variability of RR and QT intervals and QT dynamicity in patients with chronic heart failure

Relationship of QT interval variability to heart rate and RR interval variability

QT-RR hysteresis is characterized by longer QT intervals at a given RR interval while heart rates are increasing during exercise and shorter QT intervals at the same RR interval while heart rates are decreasing during recovery. It has been attributed to a lagging QT response to different directional changes in RR interval during exercise and recovery. Twenty control subjects (8 males, age 51 ± 6 yr), 16 subjects with type 2 diabetes (12 males, age 56 ± 8 yr), 71 subjects with coronary artery disease (CAD) and preserved left ventricular ejection fraction (LVEF) (≥50%) (51 males, age 59 ± 12 yr), and 17 CAD subjects with depressed LVEF (<50%) (13 males, age 57 ± 10 yr) underwent two 16-min exercise tests followed by recovery. In session 2, parasympathetic blockade with atropine (0.04 mg/kg) was achieved at end exercise. QT-RR hysteresis was quantified as: 1) the area bounded by the QT-RR relationships for exercise and recovery in the range of the minimum RR interval at peak exercise to the minimum RR interval + 100 ms and 2) the difference in QT interval duration between exercise and recovery at the minimum RR interval achieved during peak exercise plus 50 ms (ΔQT). The effect of parasympathetic blockade was assessed by substituting the QT-RR relationship after parasympathetic blockade. QT-RR hysteresis was positive in all groups at baseline and reversed by parasympathetic blockade (P < 0.01). We conclude that QT-RR hysteresis is not caused by different directional changes in RR interval during exercise and recovery. Instead, it is predominantly mediated by differential autonomic nervous system effects as the heart rate increases during exercise vs. as it decreases during recovery.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

Changes in reaction times (RTs) during attentional processing may be associated with the state of breathing. Although breathing and cardiac activity interact, the functional importance of the cardiorespiratory system for modulating attentional processing remains unclear. To determine the involvement of respiration and RR interval (RRI) variability in successful task performance, thirty-six healthy participants performed a short-term memory task. For RTs limited to correct responses, increases were observed under two conditions: i) when inspiratory onset (or exhalation-to-inhalation transition) occurred and ii) when RRI velocity increased during retrieval. Importantly, multilevel model analysis revealed that the timing of inspiratory onset and the increase in RRI velocity were temporally mismatched, suggesting that at least two independent mechanisms may prolong RT. These findings contribute to a better understanding of how respiration and RRI variability may be functionally differentiated and how each could be linked to attentional processes that influence performance.

Objective:We assessed the delay times in the interaction between the autonomic regulatory loop of Heart Rate Variability (HRV) and autonomic regulatory loop of photoplethysmographic waveform variability (PPGV), showing low-frequency oscillations.Material and Methods:In eight healthy subjects aged 25–30 years (3 male, 5 female), we studied at rest (in a supine position) the simultaneously recorded two-hour signals of RR intervals (RRIs) chain and finger photoplethysmogram (PPG). To extract the low-frequency components of RRIs and PPG signal, associated with the low-frequency oscillations in HRV and PPGV with a frequency of about 0.1 Hz, we filtered RRIs and PPG with a bandpass 0.05-0.15 Hz filter. We used a method for the detection of coupling between oscillatory systems, based on the construction of predictive models of instantaneous phase dynamics, for the estimation of delay times in the interaction between the studied regulatory loops.Results:Averaged value of delay time in coupling from the regulatory loop of HRV to the loop of PPGV was 0.9±0.4 seconds (mean ± standard error of the means) and averaged value of delay time in coupling from PPGV to HRV was 4.1±1.1 seconds.Conclusion:Analysis of two-hour experimental time series of healthy subjects revealed the presence of delay times in the interaction between regulatory loops of HRV and PPGV. Estimated delay time in coupling regulatory loops from HRV to PPGV was about one second or even less, while the delay time in coupling from PPGV to HRV was about several seconds. The difference in delay times is explained by the fact that PPGV to HRV response is mediated through the autonomic nervous system (baroreflex), while the HRV to PPGV response is mediated mechanically via cardiac output.

Vagal modulation of RR intervals during head-up tilt and the infusion of isoproterenol

The Detrended Fluctuation Analysis (DFA) is widely employed to quantify the fractal dynamics of R-R intervals (RRI). This is usually done by estimating a short- and a long-term coefficient, but it is still unclear how much the information provided by such a bi-scale DFA is independent of that of traditional spectral indices. However, more sophisticated DFA approaches have been recently proposed, including the multifractal-multiscale DFA and the DFA for magnitude and sign of RRI changes. The aim of our work is to investigate whether novel DFA approaches allow extracting the information on the nonlinear RRI dynamics that traditional spectral methods cannot retrieve.We selected 4-hour segments of beat-by-beat RRI series from a 24-hour Holter recording, one during daytime (wake), one at night (sleep) in a healthy volunteer. From the wake segment, we generated 100 surrogate series shuffling the phases but preserving the power spectrum, and then from each of the resulting RRI series, we generated the series of the sign and the series of the magnitude of successive RRI changes. We generated similar series from the sleep recording. Thus, we finally obtained 6 original beat-to-beat series to be compared with 600 surrogate series, each of 4-hour duration.The comparison between original and surrogate series showed that for this experimental setting, the traditional monofractal DFA provides the same information retrievable by the power spectrum. However, specific components of the multifractal DFA reveal information not detectable by the power spectrum, particularly in the sleep condition. Furthermore, the DFA of the magnitude of RRI changes reflects important nonlinear components. Therefore, these more sophisticated DFA approaches might effectively improve the clinical value of RRI variability analysis.

Introduction: An analysis of Heart Rate Variability (HRV) is widely used in clinical and research. To properly calculate HRV parameters, the RR intervals series must be properly preprocessed. There are already automated tools for correcting these artifacts; however, they are not fully transparent and fully customizable. To address these limitations, we introduce CoRRection, a semi-automatic tool for RR interval correction, which integrates both automatic and manual approaches providing greater control over which intervals are corrected. Material and Methods: The application offers detection and correction methods. Additionally, it allows to manually remove an artifact before applying a correction method. It also allows to clean artifacts in shorter segments which enables applying different detection methods in one example, e.g., gathered during intense exercise. The application provides a test to assess signal stationarity (Augmented Dickey-Fuller test). After signal processing the report is created, containing the number of probes deleted and modified. Results: The proposed CoRRection application allows both technical and non-technical users to preprocess RR signals with a better control over the process by enabling the use of semi-automatic approach. A few examples of studies which required Correction’s unique approach of semi-automatic correction were presented. The need for different methods and not only considering different examinations, but also different segments within one recording was emphasized. Conclusions: We have presented a new application designed to preprocess RR intervals signal with a few popular detection and correction methods. This approach enables a good compromise between a precise manual approach and a less time-consuming automatic approach. The semi-automatic method of artifact correction empowers users to explore multiple identification and correction methods (and to choose the one best fitted to the data or measurement conditions), and offers a better understanding of the examination preprocessing tools. This may also result in a better trust in the automatic approach among the users.

In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of ≥80%.