Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis

Esophageal PP intervals for analysis of short-term heart rate variability in patients with atrioventricular block before and after insertion of a temporary ventricular inhibited pacemaker

In heart rate variability (HRV) measurement, a Holter monitor is typically used to observe ECG data. However, since this device lacks mobility, any alternative wearable devices with a similar capability of recording cardiac data are preferred. On the other hand, the application provided alongside wearable devices are mostly installed in smartphone environments, which cause significant concern in user's privacy. It is better to develop our application to calculate sensitive data. This study proposed an application that utilizes a commercialized wearable wireless device to collect electrocardiography data and performs an HRV measurement using time domain, frequency domain, and nonlinear domain. Furthermore, it also examines RR interval and ECG data produced by Polar H10. This study is done by prototyping and experimenting involving participants in a laboratory environment. As a result, the proposed application successfully extracts several pieces of information based on HRV measurement. Using T-test shows data RR interval data and ECG data produced by Polar H10 are equal. Thus, RR interval data is suggested for HRV measurement instead of ECG data from the device. Because in ECG data produced by Polar H10 consist of noise especially in early recording. The benefit of using this application is the ability to conducting real-time and continuous measurement cardiac data based on HVR methods.

In semiconductor manufacturing processes, virtual metrology (VM) has been investigated as a promising tool to predict important characteristics of products. Although partial least squares (PLS) is a well-known modeling technique that can cope with collinearity and therefore applied to construction of VM, its prediction performance deteriorates due to changes in process characteristics. In particular, maintenance of equipment strongly affects the process characteristics and the prediction performance. In this paper, VM was developed by using locally weighted PLS (LW-PLS), which is a type of just-in-time modeling technique, and it was used to predict the etching conversion differential of an actual dry etching process. The industrial application results have shown that the developed VM based on LW-PLS is superior to the conventional VM based on the sequential update model and artificial neural network model. In particular, it has been confirmed that the LW-PLS-based VM can keep its high prediction performance even after the maintenance, i.e., replacement of parts.

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.

Heart rate variability (HRV) analysis from 24-hour ambulatory ECG has been widely used in risk stratification of patients after myocardial infarction (MI). The accuracy of HRV assessment is known to potentially vary when different commercial systems are used. However, the consistency of HRV measurements has never been fully investigated. Twenty-six post-MI patients (mean age 59 +/- 8 years, 22 men) were studied, of whom 13 succumbed to sudden cardiac death (SCD) within 1 year and 13 remained alive for at least 3 years (MI survivors). Each patient had a 24-hour Holter ECG recorded before hospital discharge. HRV analysis was performed four times from the same recordings using three different Holter tape analysis systems (Marquette, Reynolds, and CardioData) by four independent operators (CardioData system was used twice, once in the United Kingdom and once in the United States). Mean normal-to-normal RR intervals (mNN) and 3 HRV parameters (SDNN, RMSSD, and HRV triangular index [HRVi]) were derived from each recording. The consistency of mNN and HRV measurements was evaluated by coefficient of variance (CV) and by the Bland-Altman method. The results demonstrated that (1) all indices measured by different systems were statistically similar (P = NS) except the measurement of RMSSD (P = 0.01), (2) the measurements of mNN were highly reproducible with a maximum mean difference of 1.8 +/- 13.8 ms and maximum limits of agreement from -14.6 to +15.6 ms. The maximum mean differences were--1.8 +/- 1.4 unit and 4.4 +/- 9.6 ms for HRVi and SDNN, respectively, and RMSSD was less reproducible with a maximum mean difference of--11.1 +/- 11.5 ms, and limits of agreement from -16.2 to +9.6 ms; and (3) the consistency of mNN (CV 0.9% +/- 0.9%) was significantly higher than that of HRVi, SDNN, and RMSSD (P < 0.0001). The consistency of HRVi was similar to that of SDNN (4.8% +/- 2.1% vs 5.7% +/- 4.8%, P = 0.4), and the consistency of RMSSD (26.6% +/- 13.3%) was significantly lower than that of the other measurements (P < 0.00001). In conclusion, the measurements of mNN by different analytical systems are the most consistent among the parameters studied. The global 24-hour measurements of HRV (SDNN and HRVi) are highly reproducible, whereas the measurement of short-term HRV components (RMSSD) is significantly less reproducible.

Recent studies report that heart rate variability (HRV) is related to increased self-control abilities, greater social skills, and fatigue where the decision support systems often use RR interval signal data directly. The RR interval data filtering is compared by using different data sets and artefact removal methods when data were recorded during various training intensity. The results of artefact analysis indicate the intensity of the exercise which increases as the amount of artefact RR interval data increase. Furthermore, for artefact evaluation and fatigue estimation the Poincare plots were selected. The SD1 and SD2 indexes (standard deviation in two orthogonal directions of the Poincare plot) carry similar information to the spectral analysis and simple statistical means (like average pulse and standard error) but are easier calculated and lesser stationarity dependence. In this article Poincare diagram parameters were used to analyze HRV parameters during five stage training sessions for stages with less signal artefacts and evaluate the training impact on human fatigue. The process of HRV signal analysis described in this article is a basis for RR data processing which is important in parameter expertise and method repeatability. Systematic comparison of multiple signal sources enables different RR interval sensor evaluation based on their signal artefacts by using current findings. This research contains different methods of HRV analysis that will be used to support development of a mobile fatigue evaluation system.

Influence of high-frequency bandwidth on heart rate variability analysis during physical exercise

Physiology of brain death is characterized by major disturbances of autonomic nervous system (ANS) activity which can lead to graft dysfunction. These findings exhibit the importance of early diagnosis of brain death to improve transplantation outcome. The aim of this prospective study was to assess whether heart rate variability (HRV) analysis, a noninvasive method to investigate ANS activity in comatose patients, could achieve this goal. A total of 14 brain-injured patients were included in the study as soon as they exhibited the clinical signs of imminent brain death. The electrocardiogram was then recorded from two leads with a Holter digital monitor. The clinical diagnosis of brain death was considered after an autonomic storm had occurred. HRV was assessed from 6 h before to 6 h after brain death in both time domain and spectral analysis, estimating either global ANS activity (index of variability, total power), parasympathetic activity (percentage of delta of R-R interval >50 ms, root mean square for successive interval differences, LnHF) or sympathetic activity (LnLF). Hourly averages of these variables were compared by using one-way analysis of variance. To assess whether HRV could per se diagnose brain death, receiver operating characteristic curves were generated for total power, root mean square for successive interval differences, and LnHF. We observed, for 6 h before brain death, a progressive extinction of the influence of the ANS on cardiovascular regulation. There was no activity in the two components of the ANS as soon as brain death occurred. HRV analysis appeared to be a very sensitive but a less specific method of diagnosing brain death. A total of 14 brain-injured patients with the clinical criteria of imminent brain death were enrolled for electrocardiogram recording and heart rate variability analysis (a noninvasive method to investigate autonomic nervous system activity). For 6 h before brain death, we observed a progressive extinction of autonomic nervous system activity which was not present as soon as brain death was clinically evoked.

Objective This study investigates the relationship between depression, autonomic dysfunction, inhibitory control (IC), and reaction time by analyzing heart rate variability (HRV) during a cognitive task.Methods A total of 29 healthy males and 25 males diagnosed with depression (aged 20–35 years) participated. HRV data were recorded during the Conners Continuous Performance Test-II (CCPT-II) in each group. HRV parameters, including mean RR intervals, standard deviation of normal-to-normal heartbeats (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF), were analyzed and correlated with IC (d’) and reaction time.Results The depression group exhibited significantly lower lnHF values compared to the healthy group. SDNN and lnLF decreased in both groups during CCPT-II. In the healthy group, d’ correlated significantly with SDNN, lnLF, and lnHF at t14 and across the test duration. However, in the depression group, only RR intervals correlated with d’. A significant correlation between reaction time and HRV was noted at t14 in the healthy group, suggesting autonomic nervous system (ANS) involvement in cognitive performance.Conclusion Reaction time in healthy individuals correlated with ANS function during later stages of CCPT-II, whereas depression disrupted this association. The lower d’ in the depression group was not due to a speed-accuracy trade-off but rather a more pronounced neural network impairment. These findings suggest that depression impairs both IC and autonomic regulation.

Biomedical signals carry important information about the behaviour of the living systems under study. A proper processing of these signals in principle enhances their physiological and clinical information. Analysis of variations in the instantaneous heart rate time series using the beat to-beat RR intervals (the RR tachogram) is known as heart rate variability (HRV) analysis. Sample entropy (SampEn), refined version of approximate entropy (ApEn), is a nonlinear complexity measure used to quantify the irregularity of a RR interval time series without biasing. An increase in SampEn is an indicator of increases in complexity. Linear HRV parameters are very sensitive to ECG sampling frequency and low sampling frequency may result in clinically misinterpretation of HRV. In this study consequences of errors in SampEn based HRV induced by ECG sampling frequency have been investigated. The error induced in SampEn based HRV was found to be a function of ECG sampling frequency and RR interval data length. The relative error in SampEn was approximately 3.5%.for medium and long term data (N=500, 1000 respectively) and less than 2% for short term data (N=200) at low ECG sampling frequency of 125 Hz with respect to reference values at 2000 Hz. Therefore the SampEn based HRV indices computed from RR interval time series with low ECG sampling should be regarded with caution. The finding of this study can be partly used as a reference for the optimal ECG sampling frequency for the SampEn-based HRV assessment.

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%.

The time course and concentration-effect relationship of parasympatholytic effects of three anticholinergic drugs were investigated using spectral analysis of heart rate (HR) variability. Single intravenous (i.v.) doses of atropine (10 microg/kg), glycopyrrolate (5 microg/kg), scopolamine (5 microg/kg), and placebo were given to eight healthy volunteers in a double-blind, randomized cross-over study. Electrocardiogram (ECG) was recorded at baseline and 2.5, 5, 10, 20, and 30 minutes, and 1, 1.5, 2, 3, 4, 5, and 6 hours after drug administration, while the subjects breathed at a fixed 0.25 Hz frequency. The powers of two frequency bands (low frequency [LF] = 0.07-0.15 Hz and high frequency [HF] = 0.15-0.40 Hz) were calculated using stationary time series of R-R intervals (RRI) free from ectopic beats. To perform pharmacokinetic-pharmacodynamic (PK-PD) modeling, venous plasma drug concentrations were measured. Atropine and glycopyrrolate, and, to a lesser extent, scopolamine induced decreases in HF power and increases in LF/HF ratio of HR variability, indicating parasympatholytic activity and corresponding changes in sympathovagal balance. Maximal average decreases in HF power were 99%, 94%, and 82%, respectively, but in two scopolamine subjects, a parasympathomimetic effect was dominant. Interindividual variability was least for the Hayano index of HF power (square root (RRI HF-power)/RRI*100), and profound and consistent decreases were seen after atropine and glycopyrrolate. Pharmacokinetics were best fitted to a two-compartment open model, and effect compartment link modeling using the Hayano index was performed with the atropine and glycopyrrolate data. The best description of the PK-PD relationship for both drugs was achieved using the sigmoidal Emax model. Mean (+/-SD) EC50, sigmoidicity factor (gamma), and equilibration rate constant (k(e0)) estimates were 1.35 (+/-0.27) ng/mL, 6.07 (+/-1.98) and 11.0 (+/-5.28) l/h for atropine and 1.35 (+/-0.49) ng/mL, 4.34 (+/-1.55) and 2.26 (+/-0.81) l/h for glycopyrrolate. Spectral analysis of HR variability appears to be a powerful tool in monitoring parasympatholytic drug activity. A sigmoidal Emax model with an extremely steep concentration-response relationship was revealed for atropine and glycopyrrolate. The effects of scopolamine were more incongruous.

Background: To evaluate the health effects of air pollution on a national scale, national land use regression (LUR) models is required. However, the traffic volume, population size, or land usage are different between urban and rural areas. The predictability of the national LUR model for rural areas is still not clear. Thus, we built a national and local LUR model for NO2 in Japan, and compared the predictability.Method: We first, built a national-LUR model based on 1678 routine monitoring data for Nitrogen dioxide (NO2). Next, according to the tertile of the prefectural population size, we classified the monitoring sites into three groups. Then we built an urban-LUR (site n=1091), a middle-LUR (n=346), and a rural-LUR model (n=241). We calculated the predictability of each model and compared the predicted concentrations from each model with measured concentrations.Results: The predictability for NO2 concentrations of the national-LUR model was similar to that of the urban-LUR models: adjusted R2 = 0.75 and 0.75, respectively, and root mean square error (RMSE)=3.6 and 3.7 ppb, respectively. Adjusted R2 for the middle-LUR and the rural-LUR models were 0.66 (RMSE=3.2 ppb) and 0.70 (RMSE=2.8 ppb). Although the predictabilities of local LUR models were lower than the national-LUR models, RMSEs of local LUR models were smaller than RMSE in the national-LUR model. Also, the correlation coeffects between predicted and measured concentration in local models were higher than those in the national-LUR model. When concentrations were predicted by national-LUR in the rural area, the correlation coefficient was 0.81. It was 0.84 for the concentrations estimated by the rural-LUR model. Conclusion: The local LUR models might be more represented by the local land use conditions comparing with the national scale. Instead of population size, for improving the local model, the similarity might be evaluated by using a machine learning technique.

Heart rate variability (HRV) analysis, which is providing information about the autonomic nervous system with noninvasive method, performed with using digitized electrocardiogram records. In this study, whether HRV analysis can be done from heart sounds (which are recorded with electronic stethoscopes) has been investigated. For this purpose, an algorithm has been developed for detecting the consecutive heart time period from simultaneously recorded heart sounds and ECG. Some standard HRV parameters are calculated and compared from the HRV derived from ECG and heart sound signals. The experimental results show that, both HRV parameters derived from the heart sounds while the resting position and the ECG are very close. In conclusion, they can be used in HRV analysis.

In spite of all advances made in medicine, the perioperative environment remains a high-risk environment for patients. Perioperative organ injury, including mainly cardiovascular complications, is a major threat and often only detected at a later stage. Cardiovascular risk assessment, executed before surgery, translates into the need for advanced monitoring in high-risk patients. Evidence reveals that a comprised autonomic nervous system (ANS) has a strong correlation with cardiovascular events. Therefore it could be useful to assess ANS activity in the perioperative period as a risk indicator. The purpose of the present study was to observe if patients in high-risk groups (with American Society of Anesthesiologists (ASA) score above 1) show different patterns in their perioperative ANS activity, accessed by heart rate variability (HRV) analysis, than patients with low risk (ASA score of 1). Results show that the general course was the same for all risk groups, with a HRV decrease during anesthesia followed by an increase in the recovery period. However, the high frequency power of patients with ASA=1 seemed to be elevated in the post-anesthesia care unit, suggesting a higher parasympathetic activity during recovery than in patients with ASA > 1. Although a difference in HRV could be observed between low-risk and high-risk patients, the HRV was not sufficient as input for a classification algorithm. For a more detailed evaluation in the future, more patients need to be studied with the aim to identify if ANS monitoring may suit a novel surrogate marker for risk-assessment inthe perioperative period.