A Time-Varying Non-Parametric Methodology for Assessing Changes in QT Variability Unrelated to Heart Rate Variability

OBJECTIVE: To propose and test a novel methodology to measure changes in QT interval variability (QTV) unrelated to RR interval variability (RRV) in non-stationary conditions. METHODS: Time-frequency coherent and residual spectra representing QTV related (QTVrRRV) and unrelated (QTVuRRV) to RRV, respectively, are estimated using time-frequency Cohen's class distributions. The proposed approach decomposes the non-stationary output spectrum of any two-input one-output model with uncorrelated inputs into two spectra representing the information related and unrelated to one of the two inputs, respectively. An algorithm to correct for the bias of the time-frequency coherence function between QTV and RRV is proposed to provide accurate estimates of both QTVuRRV and QTVrRRV. Two simulation studies were conducted to assess the methodology in challenging non-stationary conditions and data recorded during head-up tilt in 16 healthy volunteers were analyzed. RESULTS: In the simulation studies, QTVuRRV changes were tracked with only a minor delay due to the filtering necessary to estimate the non-stationary spectra. The correlation coefficient between theoretical and estimated patterns was >0.92 even for extremely noisy recordings (SNR in QTV =-10dB). During head-up tilt, QTVrRRV explained the largest proportion of QTV, whereas QTVuRRV showed higher relative increase than QTV or QTVrRRV in all spectral bands (P<0.05 for most pairwise comparisons). CONCLUSION: The proposed approach accurately tracks changes in QTVuRRV. Head-up tilt induced a slightly greater increase in QTVuRRV than in QTVrRRV. SIGNIFICANCE: The proposed index QTVuRRV may represent an indirect measure of intrinsic ventricular repolarization variability, a marker of cardiac instability associated with sympathetic ventricular modulation and sudden cardiac death

Respiratory Rate Derived from Pulse Photoplethysmographic Signal by Pulse Decomposition Analysis

A novel technique to derive respiratory rate from pulse photoplethysmographic (PPG) signals is presented. It exploits some morphological features of the PPG pulse that are known to be modulated by respiration: amplitude, slope transit time, and width of the main wave, and time to the first reflected wave. A pulse decomposition analysis technique is proposed to measure these features. This technique allows to decompose the PPG pulse into its main wave and its subsequent reflected waves, improving the robustness against noise and morphological changes that usually occur in long-term recordings. Proposed methods were evaluated with a data base containing PPG and plethysmography-based respiratory signals simultaneously recorded during a paced-breathing experiment. Results suggest that normal ranges of spontaneous respiratory rate (0.1-0.5 Hz) can be accurately estimated (median and interquartile range of relative error less than 5%) from PPG signals by using the studied features

Non-linear analysis of heart rate variability and its application to predict hypotension during spinal anesthesia for cesarean delivery

A non-linear analysis of heart rate variability is carried out through two complexity measures (Correlation Dimension and Pointwise Correlation Dimension) and two regularity measures (Approximate Entropy and Sample Entropy) in order to predict hypotension episodes occurred during spinal anesthesia in cesarean delivery. These methods are applied to RR-interval series, during which woman adopts two alternative positions, one physiologically relaxed (PR) and one physiologically stressed (PS). Results show that women who developed hypotension have significantly higher (p-value = 0.05) complexity measures at PR position, (and significantly lower values for the PS position), than those who did not developed the disease. Regarding the regularity measures, women who developed hypotension have lower values, but not arriving to significance, during PS position than those who did not developed it, whereas those values remain almost constant for PR position

Photoplethysmogram Signal Morphology-Based Stress Assessment

Stress is a healthy natural response to a perceived or actual threat. However, when stress is persistent, it may decrease work productivity, increase the risk of diseases, and affect the quality of life. Stress is reflected in physiological variables, such as heart rate, blood pressure, and pulse wave velocity among others. A photoplethys-mogram (PPG) contains information related to pulse rate and blood pressure. This study analyses parameters derived from PPG signal morphology for mental stress assessment.A low-complexity algorithm is designed using bandpass filtered higher-order derivatives of the PPG signal for estimation of six morphological parameters: the forward pulse wave amplitude A1, the systole and diastole durations T1 and Td, the time delays of reflected waves T12 and T13 from the renal and iliac sites in the central arteries, and the pulse duration Tp. The parameters were investigated on a set of 18 healthy subjects by using a modified Trier Social Stress Test.The results show that the most sensitive PPG morphology parameters to mental stress are the amplitude of forward wave A1, the duration of diastole Td, the time delay of the reflected wave T13, and the pulse-to-pulse interval Tp

Human emotion characterization by heart rate variability analysis guided by respiration

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDeveloping a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio. Then, the proposed method was applied to discriminate emotions in a database of video-induced elicitation. Five emotional states, relax, joy, fear, sadness and anger, were considered. The maximum correlation between HRV and respiration spectra discriminated joy vs. relax, joy vs. each negative valence emotion, and fear vs. sadness with p-value = 0.05 and AUC = 0.70. Based on these results, human emotion characterization may be improved by adding respiratory information to HRV analysis.Peer ReviewedPostprint (author's final draft

Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients

The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA

OPTICAL SPECTROSCOPIC OBSERVATIONS OF GAMMA-RAY BLAZAR CANDIDATES. VI. FURTHER OBSERVATIONS FROM TNG, WHT, OAN, SOAR, AND MAGELLAN TELESCOPES

Indexación: Web of ScienceBlazars, one of the most extreme classes of active galaxies, constitute so far the largest known population of.-ray sources, and their number is continuously growing in the Fermi catalogs. However, in the latest release of the Fermi catalog there is still a large fraction of sources that are classified as blazar candidates of uncertain type (BCUs) for which optical spectroscopic observations are necessary to confirm their nature and their associations. In addition, about one-third of the gamma-ray point sources listed in the Third Fermi-LAT Source Catalog (3FGL) are still unassociated and lacking an assigned lower-energy counterpart. Since 2012 we have been carrying out an optical spectroscopic campaign to observe blazar candidates to confirm their nature. In this paper, the sixth of the series, we present optical spectroscopic observations for 30 gamma-ray blazar candidates from different observing programs we carried out with the Telescopio Nazionale Galileo, William Herschel Telescope, Observatorio Astronomico Nacional, Southern Astrophysical Research Telescope, and Magellan. Telescopes. We found that 21 out of 30 sources investigated are BL Lac objects, while the remaining targets are classified as flat-spectrum radio quasars showing the typical broad emission lines of normal quasi-stellar objects. We conclude that our selection of gamma-ray blazar. candidates based on their multifrequency properties continues to be a successful way to discover potential low-energy counterparts of the Fermi. unidentified gamma-ray sources and to confirm the nature of BCUs.http://iopscience.iop.org/article/10.3847/0004-6256/151/4/95/met

BL Lacertae identifications in a ROSAT-selected sample of Fermi unidentified objects

The optical spectroscopic followup of 27 sources belonging to a sample of 30 high-energy objects selected by positionally cross correlating the first Fermi/LAT Catalog and the ROSAT All-Sky Survey Bright Source Catalog is presented here. It has been found or confirmed that 25 of them are BL Lacertae objects (BL Lacs), while the remaining two are Galactic cataclysmic variables (CVs). This strongly suggests that the sources in the first group are responsible for the GeV emission detected with Fermi, while the two CVs most likely represent spurious associations. We thus find an 80% a posteriori probability that the sources selected by matching GeV and X-ray catalogs belong to the BL Lac class. We also show suggestions that the BL Lacs selected with this approach are probably high-synchrotron-peaked sources and in turn good candidates for the detection of ultra-high-energy (TeV) photons from them.Comment: 16 pages, 9 figures, 4 tables, one appendix, accepted for publication on A&A, main journal. Tables 1-3 and Figures 2-6 will only be published in the electronic edition of the journa