Juan Bolea

Methodological Framework for Estimating the Correlation Dimension in HRV Signals

This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. Each approach for slope estimation leads to a correlation dimension estimate, called D^2, D^2⊥, and D^2max. D^2 and D^2max estimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, and D^2⊥ with 1%. The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension. D^2 keeps the 81% of accuracy previously described in the literature while D^2⊥ and D^2max approaches reach 91% of accuracy in the same database.

BioSigBrowser, biosignal processing interface

This paper presents a user-friendly interface in Matlab®, called BioSigBrowser, that aims to facilitate the use of algorithms in biomedical signal processing. It includes methods related with cardiovascular signal processing, namely some multimodal analysis. This platform can treat a single signal or work in a batch mode on a given database as is usual in research. Furthermore, its modular characteristic allows easy incorporation of new methods.

Human emotion recognition using heart rate variability analysis with spectral bands based on respiration.

The work presented in this paper aims at assessing human emotion recognition by means of the analysis of the heart rate variability (HRV) with varying spectral bands based on respiratory frequency (RF). Three specific emotional states are compared corresponding to calm-neutral state (Relax), positive elicitation (Joy) and negative elicitation (Fear). Standard HRV analysis in time and frequency domain is performed. In order to better characterize the HRV component related to respiratory sinus arrhythmia, the high frequency (HF) band is centered on RF. Results reveal that the power content in low band (PLF), the normalized power content in HF band (PHFn) and the sympathovagal ratio (LF/HF) can be suitable indices to distinguish Relax and Joy. Mean heart rate and RF are significantly different between Relax and Fear. Different HRV indices show significant differences between Joy and Fear, such as pNN50, PLF, PHFn and LF/HF. Statistical analysis of HRV indices with HF centered in the RF results in a lower p-value than the ones with a HF standard band.

Microgravity effects on ventricular response to heart rate changes

The effect of simulated microgravity on ventricular repolarization (VR) has been evaluated on healthy volunteers by a 5-day Head Down (-6°) Bed Rest (HDBR) maneuver. QT to RR and QTp (measured until the peak of the T wave) to RR hystereses have been measured during a tilt table test, and differences between them have been studied to better understand possible changes in the final part of the repolarization. To characterize the hystereses, two indices have been computed: M90, quantifying adaptation lag in beats, and α evaluating the slope of parabolic regression fitting. Significant differences between QT and QTp were found before, but not after HDBR. Specifically, before HDBR was considerable lower for QTp than for QT, while α was significantly higher. After HDBR, M90 and a took essentially the same values for QT and QTp. This fact evidenced the different effect of HDBR on QT to RR and QTp to RR adaptations, and suggest HDBR could lead to an impairment in ventricular repolarization dispersion.

Influence of heart rate in non-linear HRV indices as a sampling rate effect evaluated on supine and standing

The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position (p < 0.05), concomitant with a 28% increase in mean HR (p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes.

Non-linear HRV indices under autonomic nervous system blockade

Heart rate variability (HRV) has been studied as a non-invasive technique to characterize the autonomic nervous system (ANS) regulation of the heart. Non-linear methods based on chaos theory have been used during the last decades as markers for risk stratification. However, interpretation of these nonlinear methods in terms of sympathetic and parasympathetic activity is not fully established. In this work we study linear and non-linear HRV indices during ANS blockades in order to assess their relation with sympathetic and parasympathetic activities. Power spectral content in low frequency (0.04-0.15Hz) and high frequency (0.15-0.4Hz) bands of HRV, as well as correlation dimension, sample and approximate entropies were computed in a database of subjects during single and dual ANS blockade with atropine and/or propranolol. Parasympathetic blockade caused a significant decrease in the low and high frequency power of HRV, as well as in correlation dimension and sample and approximate entropies. Sympathetic blockade caused a significant increase in approximate entropy. Sympathetic activation due to postural change from supine to standing caused a significant decrease in all the investigated non-linear indices and a significant increase in the normalized power in the low frequency band. The other investigated linear indices did not show significant changes. Results suggest that parasympathetic activity has a direct relation with sample and approximate entropies.

Reliability of Lagged Poincaré Plot Parameters in Ultrashort Heart Rate Variability Series: Application on Affective Sounds

The number of studies about ultrashort cardiovascular time series is increasing because of the demand for mobile applications in telemedicine and e-health monitoring. However, the current literature still needs a proper validation of heartbeat nonlinear dynamics assessment from ultrashort time series. This paper reports on the reliability of the Lagged Poincaré Plot (LPP) parameters—calculated from ultrashort cardiovascular time series. Reliability is studied on simulated as well as on real RR series. Simulated RR series are generated and LPP parameters estimated for ultrashort time series (from 15 to 60 s) are compared to those estimated from 1 h. All LPP parameters estimated from time series longer than 35xa0s presented a Spearmans correlation coefficient higher than 0.99. RR series acquired from 32 healthy subjects during 5-min resting state sessions are used to test the LPP approach in experimental data. The usefulness of ultrashort term parameters in real data is accomplished also studying their ability to discriminate positive and negative valence of auditory stimuli taken from the International Affective Digitized Sound System (IADS) dataset. The achieved accuracies in the recognition of elicitation along the valence dimension, using only the LPP parameters, were of 77.78% for 1 min 28 s series, and of 79.17% for 35 s series.

Heart Rate Variability in Pregnant Women before Programmed Cesarean Intervention

Background: Heart rate variability (HRV) indices have shown ability for hypotension prediction during spinal anaesthesia in pregnant women programmed for cesarean only the same day of the surgery but not the previous day. Objective: To study changes in linear and nonlinear HRV indices of pregnant women programmed for cesarean between the previous day and the surgery day. Methods: Previous day recordings (PDR) and surgery day recordings (SDR) of 71 pregnant women programmed for cesarean have been studied during the following conditions: lateral decubitus (LD), supine decubitus (SD) and Valsalva maneuver recovery (VR). Linear HRV indices include classical temporal and spectral indices. Nonlinear HRV indices consist of sample and approximate entropy and correlation dimension (D 2). Results: Some linear HRV indices show very significant increases (p < 0.01) in SDR with respect to PDR: HRM in VR, SDNN in LD and SD, power in the very low frequency band in LD, power in the low frequency band in LD and SD. On the contrary, nonlinear HRV indices show a decrease in almost every condition and index in SDR with respect to PDR, being only statistically significant for D 2 in SD (p < 0.01). Conclusions: The increase in the former linear indices and the decrease in nonlinear indices the surgery day with respect to the previous day can be attributable to the stress induced by the imminent surgery.

Heart rate and ventricular repolarization variabilities interactions modification by microgravity simulation during head-down bed rest test

Features extracted from heart rate variability (HRV) have allowed to classify several cardiac disorders. In this work, the linear relationship between HRV and ventricular repolarization variability is studied as a possible feature for stratifying risk of cardiac arrhythmias under weightlessness conditions. Three VR beat-to-beat indexes extracted from ECG signals as QT, QTp (QRS onset to T wave peak) and Tpe (peak to end of T wave) were measured. ARARX modeling was used to estimate the VR variability fraction driven by HRV. Head down bed rest as microgravity Earth-based test increased the linear dependency of VR variability driven by HRV. Furthermore, in a pairwise comparison between PRE and POST conditions showed significant differences (p-values <; 0.05) for Tpe variability contents. These results evidence a reduced recovery capacity for the Tpe re-polarization variability to restore its linear dependency values to HR in the first moments at the end of BR.

Nonlinear Dynamics of Heart Rate Variability in Children with Asthmatic Symptoms

Asthma is a chronic lung disease that is prone to start during chilhood. Although symptoms can be usually controlled with medication, early diagnosis is crucial to reduce the risk of permanent airway obstruction. Despite the fact that origin of asthma is still uncertain, abnormal parasympathetic nervous system (PNS) activity has been pointed out to play a major role in its pathogenesis. In this work the use of nonlinear heart rate variability (HRV) indexes is proposed in order to look for differences between children classified as high- or low-risk of suffering from asthma in the future. PNS activity is assessed trough a filtered HRV signal. Correlation dimension analysis showed statistically significant differences distinguishing high- and low-risk. Decreased complexity observed in high-risk group suggests that abnormal PNS activity might be related with increased risk of developing asthma.