Segments Of RR Intervals Research Articles

Effect of series of periodic limb movements in sleep on blood pressure, heart rate and high frequency heart rate variability.

The phenomenon known as periodic limb movements in sleep (PLMS) has been linked to a change in autonomic nervous system (ANS) activity and its effect on circulatory regulation. Autonomic dysfunction or dysregulation in patients with PLMS has been described in some domains; however, any relationship between heart rate variability (HRV) and PLMS has not been clearly established. HRV analysis is a recognised, non-invasive research method that describes the influence of the ANS on heart rate (HR). The aim of our study was to further investigate the dysregulation of autonomic HR control in patients with PLMS. We undertook a retrospective analysis of the polysomnographic (PSG), demographic and medical data of five patients with a total number of 1,348 PLMS. We analysed HR, HRV HF, systolic blood pressure (SBP), and diastolic blood pressure (DBP) for 10 heartbeats before the series of PLMS and 10 consecutive heartbeats as beat-to-beat measurements. The presented method of using successive, short, 10 RR interval segments refers to the time-frequency measurement, which is very clear and useful for presenting changes in the calculated parameters over time and thereby illustrating their dynamics. This method allowed us to assess dynamic changes in HRV HF during successive PLMS series. Statistical analysis was performed using IBM SPSS Statistics (v. 28.0.0.0). The Kruskal-Wallis test was performed to find statistically significant changes from baseline. No statistically significant changes in HR, SBP, or DBP were found in our group, although an increase in the value of the HRV HF was noted, suggesting an increase in intracardiac parasympathetic activity during the subsequent series of PLMS. Our study indicates an increase in parasympathetic activity during the appearance of successive PLMS, which, with the simultaneous lack of changes in HR, may suggest an increase in sympathetic activity, and therefore the appearance of so-called 'autonomic co-activation' resulting in the possibility of life-threatening cardiac events. Our findings add to the literature information regarding HRV in PLMS, and highlight the need for further studies to elucidate the effects of these conditions on the ANS, and on cardiovascular health.

Short-Term Impact of Traffic-Related Particulate Matter and Noise Exposure on Cardiac Function.

Exposure to traffic-related air pollution and noise exposure contributes to detrimental effects on cardiac function, but the underlying short-term effects related to their simultaneous personal exposure remain uncertain. The aim is to assess the impact of total inhaled dose of particulate matter and total noise exposure on the variations of electrocardiogram (ECG) parameters between pre-cycling and post-cycling periods. Mid-June 2019, we collected four participants’ personal exposure data related to traffic-related noise and particulate matter (PM2.5 and PM10) as well as ECG parameters. Several Bayesian linear models were built to examine a potential association between air pollutants and noise exposure and ECG parameters: heart rate (HR), standard deviation of the normal-to-normal intervals (SDNN), percentage of successive RR intervals that differ by more than 50 ms (pNN50), root mean square of successive RR interval differences (rMSSD), low-frequency power (LF), high-frequency power (HF), and ratio of low- to high-frequency power (LF/HF). We analyzed in total 255 5-min segments of RR intervals. We observed that per 1 µg increase in cumulative inhaled dose of PM2.5 was associated with 0.48 (95% CI: 0.22; 15.61) increase in variation of the heart rate, while one percent of total noise dose was associated with 0.49 (95% CI: 0.17; 0.83) increase in variation of heart rate between corresponding periods. Personal noise exposure was no longer significant once the PM2.5 was introduced in the whole model, whilst coefficients of the latter that were significant previously remained unchanged. Short-term exposure to traffic-related air and noise pollution did not, however, have an impact on heart rate variability.

Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals.

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Stabilization period before capturing an ultra-short vagal index can be shortened to 60 s in endurance athletes and to 90 s in university students.

PurposeTo find the shortest, acceptable stabilization period before recording resting, supine ultra-short-term Ln RMSSD and heart rate (HR).MethodThirty endurance-trained male athletes (age 24.1 ± 2.3 years, maximal oxygen consumption (VO2max) 64.1 ± 6.6 ml·kg-1·min-1) and 30 male students (age 23.3 ± 1.8 years, VO2max 52.8 ± 5.1 ml·kg-1·min-1) were recruited. Upon awaking at home, resting, supine RR intervals were measured continuously for 10 min using a Polar V800 HR monitor. Ultra-short-term Ln RMSSD and HR values were calculated from 1-min RR interval segments after stabilization periods from 0 to 4 min in 0.5 min increments and were compared with reference values calculated from 5-min segment after 5-min stabilization. Systematic bias and intraclass correlation coefficients (ICC) including 90% confidence intervals (CI) were calculated and magnitude based inference was conducted.ResultsThe stabilization periods of up to 30 s for athletes and up to 60 s for students showed positive (possibly to most likely) biases for ultra-short-term Ln RMSSD compared with reference values. Stabilization periods of 60 s for athletes and 90 s for students showed trivial biases and ICCs were 0.84; 90% CI 0.72 to 0.91, and 0.88; 0.79 to 0.94, respectively. For HR, biases were trivial and ICCs were 0.93; 0.88 to 0.96, and 0.93; 0.88 to 0.96, respectively.ConclusionThe shortest stabilization period required to stabilize Ln RMSSD and HR was set at 60 s for endurance-trained athletes and 90 s for university students.

A deep learning approach for real-time detection of atrial fibrillation

Goal: To develop a robust and real-time approach for automatic detection of atrial fibrillation (AF) in long-term electrocardiogram (ECG) recordings using deep learning (DL). Method: An end-to-end model combining the Convolutional- and Recurrent-Neural Networks (CNN and RNN) was proposed to extract high level features from segments of RR intervals (RRIs) in order to classify them as AF or normal sinus rhythm (NSR). Results: The model was trained and validated on three different databases including a total of 89 subjects. It achieved a sensitivity and specificity of 98.98% and 96.95%, respectively, validated through a 5-fold cross-validation. Additionally, the proposed model was found to be computationally efficient and it was capable of analyzing 24 h of ECG recordings in less than one second. The proposed algorithm was also tested on the unseen datasets to examine its robustness in detecting AF for new recordings which resulted in 98.96% and 86.04% for specificity and sensitivity, respectively. Conclusion: Compared to the state-of-the-art models evaluated on standard benchmark ECG datasets, the proposed model produced better performance in detecting AF. Additionally, since the model learns features directly from the data, it avoids the need for clever/cumbersome feature engineering.

Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects

Abstract It is known that patients with congestive heart failure have reduced ability to modulate heart rate in comparison with normal subjects. However, the characteristics of these changes is not well understood. This study therefore investigated the characteristic features of heart rate changes to assess how they differed between both groups. Fifty-two normal sinus rhythm subjects and 18 congestive heart failure patients from the PhysioNet database were studied. Nine common heart rate indices were studied: three time-domain indices (MEAN RR interval, standard deviation of successive RR SDNN, and square root of mean squared differences of successive RR RMSSD), three frequency-domain indices (normalized low-frequency power LFn, normalized high-frequency power HFn, and their ratio LF/HF), and three non-linear indices (vector length index VLI, vector angle index VAI and sample entropy SampEn). Two 5-min segments from every subject, neither of which had any ectopic beat, were analyzed. The statistical differences between the two clinical groups for the first and second segments, and their average were determined for all nine indices. Results showed that there was no significant difference between the two 5-min RR interval segments for any technique. All frequency-domain and non-linear indices, but only one time-domain index (SDNN), were significantly different between subject groups. However, some indices were much more sensitive to the clinical differences than others; with the best performing techniques, one non-linear index VLI and one time domain index SDNN, followed by all three frequency indices of LFn, HFn and LF/HF, and finally two of the other non-linear indices VAI and SampEn. A simple RBF SVM-based classification algorithm gave a good performance for classifying the CHF and NSR subjects. And the mean Se, Sp and Acc of SVM classifier from 10 folds were 91.31%, 90.04% and 90.95% respectively. We have shown that there are characteristic differences in heart rate changes between congestive heart failure and normal sinus rhythm, suggesting characteristic rhythm differences.

Heart rate-based window segmentation improves accuracy of classifying posttraumatic stress disorder using heart rate variability measures

Objective. Heart rate variability (HRV) characterizes changes in autonomic nervous system function and varies with posttraumatic stress disorder (PTSD). In this study we developed a classifier based on heart rate (HR) and HRV measures, and improved classifier performance using a novel HR-based window segmentation.Approach. Single-channel ECG data were collected from 23 subjects with current PTSD, and 25 control subjects with no history of PTSD over 24 h. RR intervals were derived from these data, cleaned, and used to calculate HR and HRV metrics. These metrics were used as features in a logistic regression classifier. Performance was assessed via repeated random sub-sampling validation. To reduce noise and activity-related effects, we calculated features from five non-overlapping ten-minute quiescent segments of RR intervals defined by lowest HR, as well as random ten-minute segments as a control.Main Results. Using a combination of the four most predictive features derived from quiescent segments we achieved a median area under the receiver operating curve (AUC) of 0.86 on out-of-sample test set data. This was significantly higher than the AUC using 24 h of data (0.72) or random segments (0.67).Significance. These results demonstrate our segmentation approach improves the classification of PTSD from HR and HRV measures, and suggest the potential for tracking PTSD illness severity via objective physiological monitoring. Future studies should prospectively evaluate if classifier output changes significantly with worsening or effective treatment of PTSD.

Characterization of the autonomic system during the cyclic alternating pattern of sleep.

Evaluation of the RR variability was carried out during the Cyclic Alternating Pattern (CAP) in sleep. CAP is a central phenomenon formed by short events called A-phases that break basal electroencephalogram (EEG) oscillations of the sleep stages. A-phases are classified in three types (A1, A2 and A3) based on the EEG desynchronization during A-phase. However, the relation of A-phases with other systems, such as cardiovascular system, is unclear and a deep analysis is required. For the study, six patients with Nocturnal Front Lobe Epilepsy (NFLE) and other six healthy controls patients underwent whole night polysomnographic recordings with CAP and hypnogram annotations. Amplitude reduction and time delay of the RR intervals minimum with respect to A-phases onset were computed. In addition, the same process was computed over randomly chosen RR interval segments during the NREM sleep for further comparison. The results suggest that the onset of the A-phases is correlated with a significative increase of the heart rate that peaks at around 4s after the Aphase onset, independently of the A-phase subtype.

Polymorphisms of the antiapoptotic protein bag3 may play a role in the pathogenesis of tako-tsubo cardiomyopathy

following classes this dependence progressively increased up to the extremely high level in hrv7 class. Thiswaywe have obtained a set of HRV indiceswhich are associated in different extent with HR. Such parameters constitute a perfect material for investigations of the HR contribution to the HRV prognostic power [5]. Moreover, this may be applied not only to the spectral HRV analysis but also to any other analysis of heart rate dynamics which parameters are associated with HR — in such instances the RR-interval tachograms should rather be modified (i.e. divided or multiplied by avRRs) before a given heart rate dynamics is analyzed. Importantly, if one calculates average HRV indices from the indices corresponding to short RR interval segments (e.g. from Holter recordings), one should first obtain the global average HRV indices of all RR interval segments and then divide or multiply them by a global avRR — the division or multiplication for each segment separately may create the situation where a single RR interval segment with unusually slow or fast HR determines the results for a given patient (it is especially likely if one employs high powers of avRR). To conclude, the described approach opens new perspectives for the analysis of HR dynamics, i.e. it makes possible to separate clinical (e.g. prognostic) information coming from average heart rate and from its dynamics.

Autonomic effects on the spectral analysis of heart rate variability after exercise

Although frequency-domain analysis of heart rate variability (HRV) has been performed in the setting of exercise and recovery from exercise, the relationship of specific frequency components to sympathetic and parasympathetic inputs has not been validated in this setting. The aim of this study is to evaluate the relationship of frequency components of HRV to sympathetic and parasympathetic modulation in the setting of recovery after exercise using selective autonomic blockade. Normal subjects (n = 27, 17 men, 53 +/- 7 yr old) underwent bicycle stress testing on four separate days. On day 1, a baseline study without autonomic blockade was performed. On days 2 through 4, either beta-adrenergic, parasympathetic, or double blockade was administered during exercise and completed 3 min before recovery. Continuous ECG was recorded for 5 min starting from the end of exercise. Time- and frequency-domain measures of HRV were computed for each of the five 1-min segments of RR intervals. Parasympathetic blockade significantly decreased all the HRV measures compared with baseline (P < 0.02 for all). Root mean square of successive differences of RR intervals (rMSSD) was increased by beta-adrenergic blockade (P < 0.0002). All the HRV measures except rMSSD showed increases with time after the first minute of recovery. The low frequency-to-high frequency ratio did not respond to autonomic blockade or to recovery time, consistent with the expected changes in sympathovagal influence. Root mean square (detrended SD) and rMSSD were highly correlated with the square root of the total power (r = 0.96) and high-frequency power (r = 0.95), respectively. Although there are marked reductions in the frequency-domain measures in recovery versus rest, the fluctuations in the low- and high-frequency bands respond to autonomic blockade in the expected fashion. Time-domain measures of HRV were highly correlated with frequency-domain measures and therefore provide a computationally more efficient assessment of autonomic influences during recovery from exercise that is less susceptible to anomalies of frequency-domain analysis.

Heart rate variability derived from exercise ECG in the detection of coronary artery disease

The diagnostic performance of heart rate variability (HRV) analysis from exercise ECG in the detection of coronary artery disease (CAD) is unknown. Bicycle exercise ECG recordings from The Finnish Cardiovascular Study (FINCAVAS) of angiography-proofed CAD patients (n = 112) and a patient group with a low likelihood of CAD (n = 114) were analyzed. HRV parameters (SDNN, RMSSD, Poincaré SD1 and SD2) were calculated from 1 min segments before exercise, during exercise and after exercise. All the parameters were in addition calculated from heart rate (HR)-corrected RR-interval segments. The ST-segment depressions in each stage were also determined. The diagnostic performance of the parameters was evaluated with the area under the receiver operating characteristic (ROC) curve method. The uncorrected HRV parameters showed the best diagnostic performance in the recovery segments but the correlation with HR was also high (SDNN: 0.758/−0.64, RMSSD: 0.747/−0.60; area under the ROC/correlation coefficient). The HR correction decreased the correlation and the diagnostic performance in recovery segments (SDNN: 0.515/−0.12, RMSSD: 0.609/0.20). The diagnostic performance of ST-level at its best was higher than any of HRV parameters (ST-level: 0.795/0.36). According to the results, the HR correction decreased the diagnostic performance of the recovery phase. The HRV parameters calculated from 1 min segments of exercise test ECG were not as capable as traditional ST-segment analysis. In conclusion, the HRV analysis from exercise or recovery phase seems to be inadequate in the detection of CAD.

Reproducibility of heart rate variability, blood pressure variability and baroreceptor sensitivity during rest and head-up tilt

Previous studies have indicated moderate-to-poor reproducibility of heart rate variability (HRV) but the reproducibility of blood pressure variability (BPV) and spectral measures of baroreceptor sensitivity (BRS) are not well established. We measured normal-to-normal heart beat (RR) interval and finger blood pressure (Finapres) in 14 healthy individuals on three different days. The protocol was 1 h of supine rest and 1 h of 60-degree head-up tilt. Time-series of consecutive 300-s segments as well as 1024-s segments of RR intervals and systolic, diastolic and mean blood pressures were extracted for the assessment of day-to-day and short-term reproducibility. Power spectrum analysis (Fourier) and transfer function analysis was performed. Reproducibility was assessed using the coefficient of variation (CV). The reproducibility of the mean RR interval, mean systolic, diastolic and mean blood pressure was good (CV<10 %). However, there was only moderate-to-poor reproducibility of the spectral parameters of HRV (CV range 18-36%) and BPV (16-44%) and moderate reproducibility of BRS (14-20%). Spectral estimates of BRS had only moderate reproducibility although it was better than the spectral estimates of HRV and BPV.