Raquel Bailón

Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions

In this paper we assessed the possibility of using the pulse rate variability (PRV) extracted from the photoplethysmography signal as an alternative measurement of the HRV signal in non-stationary conditions. The study is based on analysis of the changes observed during a tilt table test in the heart rate modulation of 17 young subjects. First, the classical indices of HRV analysis were compared to the indices from PRV in intervals where stationarity was assumed. Second, the time-varying spectral properties of both signals were compared by time-frequency (TF) and TF coherence analysis. Third, the effect of replacing PRV with HRV in the assessment of the changes of the autonomic modulation of the heart rate was considered. Time-invariant HRV and PRV indices showed no statistically significant differences (p > 0.05) and high correlation (>0.97). Time-frequency analysis revealed that the TF spectra of both signals were highly correlated (0.99 +/- 0.01); the difference between the instantaneous power, in the LF and HF bands, obtained from HRV and PRV was small (<10(-3) s(-2)) and their temporal patterns were highly correlated (0.98 +/- 0.04 and 0.95 +/- 0.06 in the LF and HF bands, respectively) and TF coherence in the LF and HF bands was high (0.97 +/- 0.04 and 0.89 +/- 0.08, respectively). Finally, the instantaneous power in the LF band was observed to significantly increase during head-up tilt by both HRV and PRV analysis. These results suggest that although some differences in the time-varying spectral indices extracted from HRV and PRV exist, mainly in the HF band associated with respiration, PRV could be used as a surrogate of HRV during non-stationary conditions, at least during the tilt table test.

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the hearts electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% /spl plusmn/ 0.2%, mean /spl plusmn/ SD (0.002 /spl plusmn/ 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% /spl plusmn/ 4% (0.022 /spl plusmn/ 0.016 Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.

Analysis of Heart Rate Variability Using Time-Varying Frequency Bands Based on Respiratory Frequency

In this paper a methodological approach for the analysis of nonstationary heart rate variability (HRV) signals using time-varying frequency bands based on respiratory frequency is presented. Spectral analysis of HRV is accomplished by means of the Smoothed Pseudo Wigner Ville distribution. Different approaches to the definition of the low frequency (LF) and high frequency (HF) bands are considered which involve respiratory information, derived either from a respiratory signal or from the ECG itself. Results are presented which derive from recordings acquired during stress testing and induced emotion experiments.

PTT Variability for Discrimination of Sleep Apnea Related Decreases in the Amplitude Fluctuations of PPG Signal in Children

In this paper, an analysis of pulse transit time variability (PTTV) during decreases in the amplitude fluctuations of pulse photoplethysmography signal (PPG) (DAP) events for obstructive sleep apnea syndrome (OSAS) screening is presented. The temporal evolution of time-frequency PTTV parameters during DAP was analyzed. The results show an increase in the sympathetic activity index low-frequency component (LF) during DAP for PTTV (85%) significantly higher than for heart rate variability (HRV) (33%), (¿ < 10^-13). However, decreases in parasympathetic activity produce lower decrements in high-frequency component (HF) indexes for PTTV (18%) than for HRV (22%). Thus, PTTV reflects sympathetic changes more clearly than HRV. A clinical study was carried out. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the PTTV indexes. The ratio of DAP events per hour <i>r</i> _DAP, the ratio after filtering based on HRV indexes <i>r</i> ^HRV _DAP, or on PTTV indexes <i>r</i> ^PTTV _DAP, were computed. The results show an accuracy of 75% for <i>r</i> ^PTTV _DAP (14% increase with respect to <i>r</i> _DAP and 5% increase with respect to <i>r</i> ^HRV _DAP), a sensitivity of 81.8%, and a specificity of 73.9% when classifying 1-h polysomnographic excerpts as OSAS or normal. These results suggest that the combination of DAP and PTTV could be better alternative for sleep apnea screening using PPG with the added benefit of its low cost and simplicity.

The Integral Pulse Frequency Modulation Model With Time-Varying Threshold: Application to Heart Rate Variability Analysis During Exercise Stress Testing

In this paper, an approach for heart rate variability analysis during exercise stress testing is proposed based on the integral pulse frequency modulation (IPFM) model, where a time-varying threshold is included to account for the nonstationary mean heart rate. The proposed technique allows the estimation of the autonomic nervous system (ANS) modulating signal using the methods derived for the IPFM model with constant threshold plus a correction, which is shown to be needed to take into account the time-varying mean heart rate. On simulations, this technique allows the estimation of the ANS modulation on the heart from the beat occurrence time series with lower errors than the IPFM model with constant threshold (1.1% ± 1.3% versus 15.0% ± 14.9%). On an exercise stress testing database, the ANS modulation estimated by the proposed technique is closer to physiology than that obtained from the IPFM model with constant threshold, which tends to overestimate the ANS modulation during the recovery and underestimate it during the initial rest.

Characterization of Dynamic Interactions Between Cardiovascular Signals by Time-Frequency Coherence

An assessment of the dynamic interactions between cardiovascular signals can provide valuable information to improve the understanding of cardiovascular control. In this study, two methodologies for the characterization of time-frequency (TF) coherence between cardiovascular signals are described. The methodologies are based on the smoothed pseudo-Wigner-Ville distribution (SPWVD) and multitaper spectrogram (MTSP), and include the automatic assessment of the significance level of coherence estimates. The capability to correctly localize TF regions, where signals are locally coupled, is assessed using computer-generated data, and data from healthy volunteers. The SPWVD allows for the localization of these regions with higher accuracy (AC>;96.9% for SNR≥5 dB) than the MTSP (AC>;84.4% for SNR≥5 dB). In 14 healthy subjects, TF coherence analysis was used to describe the changes, which a tilt table test provokes in the cardiovascular control. Orthostatic stress provoked an increase in the coupling between R-R variability (RRV) and systolic arterial pressure variability; it did not provoke any significant changes in the coupling between RRV and respiration. In HF band, it decreased the strength of the coupling between RRV and pulse interval variability estimated from arterial pressure signal.

Analysis of heart rate variability during exercise stress testing using respiratory information

This paper presents a novel method for the analysis of heart rate variability (HRV) during exercise stress testing enhanced with respiratory information. The instantaneous frequency and power of the low frequency (LF) and high frequency (HF) bands of the HRV are estimated by parametric decomposition of the instantaneous autocorrelation function (ACF) as a sum of damped sinusoids. The instantaneous ACF is first windowed and filtered to reduce the cross terms. The inclusion of respiratory information is proposed at different stages of the analysis, namely, the design of the filter applied to the instantaneous ACF, the parametric decomposition, and the definition of a dynamic HF band. The performance of the method is evaluated on simulated data as well as on a stress testing database. The simulation results show that the inclusion of respiratory information reduces the estimation error of the amplitude of the HF component from 3.5% to 2.4% in mean and related SD from 3.0% to 1.7% when a tuned time smoothing window is used at an SNR of 15 dB. Results from the stress testing database show that information on respiratory frequency produces HF power estimates which closely resemble those from the simulations which exhibited lower SD. The mean SD of these estimates with respect to their mean trends is reduced by 84% (from 0.74 x 10(-3) s(-2) to 0.12 x 10(-3) s(-2)). The analysis of HRV in the stress testing database reveals a significant decrease in the power of both the LF and HF components around peak stress

Coronary artery disease diagnosis based on exercise electrocardiogram indexes from repolarisation, depolarisation and heart rate variability

Several indexes have been reported to improve the accuracy of exercise test electrocardiogram (ECG) analysis in the diagnosis of coronary artery disease (CAD), compared with the classical ST depression criterion. Some of them combine repolarisation measurements with heart rate (HR) information (such as the so-called ST/HR hysteresis); others are obtained from the depolarisation period (such as the Athens QRS score); finally, there are heart rate variability (HRV) indexes that account for the nervous system activity. The aim of this study was to identify the best exercise ECG indexes for CAD diagnosis. First, a method to automatically estimate repolarisation and depolarisation indexes in the presence of noise during a stress test was developed. The method is divided into three stages: first, a preprocessing step, where QRS detection, filtering and baseline beat rejection are applied to the raw ECG, prior to a weighted averaging secondly, a post-processing step in which potentially noisy averaged beats are identified and discarded based on their noise variance; finally, the measurement step, in which ECG indexes are computed from the averaged beats. Then, a multivariate discriminant analysis was applied to classify patients referred for the exercise test into two groups: ischaemic (positive coronary angiography) and low-risk (Framingham risk index<5%). HR-corrected repolarisation indexes improved the sensitivity (SE) and specificity (SP) of the classical exercise test (SE=90%, SP-79% against SE=65%, SP=66%). Depolarisation indexes also achieved an improvement over ST depression measurements (SE=78%, SP=81%). HRV indexes obtained the best classification results in our study population (SE=94%, SP=92%) by means of the very high-frequency power (VHF) (0.4–1 Hz) at stress peak.

Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time–frequency analysis

In this study, a framework for the characterization of the dynamic interactions between RR variability (RRV) and systolic arterial pressure variability (SAPV) is proposed. The methodology accounts for the intrinsic non-stationarity of the cardiovascular system and includes the assessment of both the strength and the prevalent direction of local coupling. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used to estimate the time-frequency (TF) power, coherence, and phase-difference spectra with fine TF resolution. The interactions between the signals are quantified by time-varying indices, including the local coupling, phase differences, time delay, and baroreflex sensitivity (BRS). Every index is extracted from a specific TF region, localized by combining information from the different spectra. In 14 healthy subjects, a head-up tilt provoked an abrupt decrease in the cardiovascular coupling; a rapid change in the phase difference (from 0.37 ± 0.23 to -0.27 ± 0.22 rad) and time delay (from 0.26 ± 0.14 to -0.16 ± 0.16 s) in the high-frequency band; and a decrease in the BRS (from 23.72 ± 7.66 to 6.92 ± 2.51 ms mmHg(-1)). In the low-frequency range, during a head-up tilt, restoration of the baseline level of cardiovascular coupling took about 2 min and SAPV preceded RRV by about 0.85 s during the whole test. The analysis of the Eurobavar data set, which includes subjects with intact as well as impaired baroreflex, showed that the presented methodology represents an improved TF generalization of traditional time-invariant methodologies and can reveal dysfunctions in subjects with baroreflex impairment. Additionally, the results also suggest the use of non-stationary signal-processing techniques to analyze signals recorded under conditions that are usually supposed to be stationary.

Influence of Running Stride Frequency in Heart Rate Variability Analysis During Treadmill Exercise Testing

The analysis and interpretation of heart rate variability (HRV) during exercise is challenging not only because of the nonstationary nature of exercise, the time-varying mean heart rate, and the fact that respiratory frequency exceeds 0.4 Hz, but there are also other factors, such as the component centered at the pedaling frequency observed in maximal cycling tests, which may confuse the interpretation of HRV analysis. The objectives of this study are to test the hypothesis that a component centered at the running stride frequency (SF) appears in the HRV of subjects during maximal treadmill exercise testing, and to study its influence in the interpretation of the low-frequency (LF) and high-frequency (HF) components of HRV during exercise. The HRV of 23 subjects during maximal treadmill exercise testing is analyzed. The instantaneous power of different HRV components is computed from the smoothed pseudo-Wigner-Ville distribution of the modulating signal assumed to carry information from the autonomic nervous system, which is estimated based on the time-varying integral pulse frequency modulation model. Besides the LF and HF components, the appearance is revealed of a component centered at the running SF as well as its aliases. The power associated with the SF component and its aliases represents 22 ± 7% (median ± median absolute deviation) of the total HRV power in all the subjects. Normalized LF power decreases as the exercise intensity increases, while normalized HF power increases. The power associated with the SF does not change significantly with exercise intensity. Consideration of the running SF component and its aliases is very important in HRV analysis since stride frequency aliases may overlap with LF and HF components.