Field-mediated locomotor dynamics on highly deformable surfaces.

Studies of active matter-systems consisting of individuals or ensembles of internally driven and damped locomotors-are of interest to physicists studying nonequilibrium dynamics, biologists interested in individuals and swarm locomotion, and engineers designing robot controllers. While principles governing active systems on hard ground or within fluids are well studied, another class of systems exists at deformable interfaces. Such environments can display mixes of fluid-like and elastic features, leading to locomotor dynamics that are strongly influenced by the geometry of the surface, which, in itself, can be a dynamical entity. To gain insight into principles by which locomotors are influenced via a deformation field alone (and can influence other locomotors), we study robot locomotion on an elastic membrane, which we propose as a model of active systems on highly deformable interfaces. As our active agent, we use a differential driven wheeled robotic vehicle which drives straight on flat homogeneous surfaces, but reorients in response to environmental curvature. We monitor the curvature field-mediated dynamics of a single vehicle interacting with a fixed deformation as well as multiple vehicles interacting with each other via local deformations. Single vehicles display precessing orbits in centrally deformed environments, while multiple vehicles influence each other by local deformation fields. The active nature of the system facilitates a differential geometry-inspired mathematical mapping from the vehicle dynamics to those of test particles in a fictitious "spacetime," allowing further understanding of the dynamics and how to control agent interactions to facilitate or avoid multivehicle membrane-induced cohesion.

Parasympathetic nervous system: a key role in control and mood disorders in patients with asthma.

BACKGROUND: Patients with severe asthma often have uncontrolled disease and experience mood disorders, particularly anxiety and depression. The autonomic nervous system plays an important role in asthma, mainly through the parasympathetic system (PANS), which favours bronchoconstriction and mental health status. OBJECTIVE: To evaluate the role of the activation of the PANS in uncontrolled asthma and mood disorders related. METHODS: Proof-of-concept cross-sectional study that analysed demographic and clinical variables reflecting asthma severity and control, lung function, inflammation (from induced sputum), evaluation of quality of life, the risk for anxiety and depression according to validated questionnaires. PANS analysis was conducted based on heart rate variability (HRV): SDNN (standard deviation of the difference between consecutive NN intervals), RMSSD (root mean square of the successive differences), pNN50 (percentage of consecutive NN intervals), TP (total power), and Pr (respiratory-related power). RESULTS: Thirty patients with asthma, grouped according to asthma control and the risk for anxiety and depression. Ten patients with uncontrolled asthma compared to the patients with controlled asthma showed significant differences (p<0.05) in SDNN (26.5 [8.2] vs 42.7 [29.7]), RMSSD (14.1 [6.5] vs 24 [20]), pNN50 (0.6 [1.5] vs 6.2 [11.8]), TP (0.0005 [0.00046] vs 0.0014 [0.00085]), and Pr (0.0003 [0.00025] vs 0.0007 [0.00060]) respectively. Thirteen patients at risk for anxiety and depression compared to the patients without showed reduced values (p<0.05) for SDNN (26.5 [7.9] vs 45.6 [31.3]), pNN50 (0.75 [1.4] to 7.12 [12.6]), TP (0.0005 [0.00048] to 0.0012 [0.0008]), and Pr (0.0003 [0.00027] to 0.0008 [0.00062]). CONCLUSION: Our results suggest that PANS activity is depressed in patients with uncontrolled asthma and common mood disorders as depression and anxiety, and the evaluation of HRV may be a useful means for follow-up of asthma control and related mood disorders.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

Effects of Missing Data on Heart Rate Variability Metrics.

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis.

OBJECTIVE: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We aimed to develop new markers based on the morphology of the QRS complex of the electrocardiogram (ECG). METHODS: ECG recordings from 29 patients undergoing hemodialysis (HD) were processed. Mean warped QRS complexes were computed in two-minute windows at the start of an HD session, at the end of each HD hour and 48 h after it. We quantified QRS width, amplitude and the proposed QRS morphology-based markers that were computed by warping techniques. Reference [K+] and [Ca2+] were determined from blood samples acquired at the time points where the markers were estimated. Linear regression models were used to estimate electrolyte levels from the QRS markers individually and in combination with T wave morphology markers. Leave-one-out cross-validation was used to assess the performance of the estimators. RESULTS: All markers, except for QRS width, strongly correlated with [K+] (median Pearson correlation coefficients, r, ranging from 0.81 to 0.87) and with [Ca2+] (r ranging from 0.61 to 0.76). QRS morphology markers showed very low sensitivity to heart rate (HR). Actual and estimated serum electrolyte levels differed, on average, by less than 0.035 mM (relative error of 0.018) for [K+] and 0.010 mM (relative error of 0.004) for [Ca2+] when patient-specific multivariable estimators combining QRS and T wave markers were used. CONCLUSION: QRS morphological markers allow non-invasive estimation of [K+] and [Ca2+] with low sensitivity to HR. The estimation performance is improved when multivariable models, including T wave markers, are considered. SIGNIFICANCE: Markers based on the QRS complex of the ECG could contribute to non-invasive monitoring of serum electrolyte levels and arrhythmia risk prediction in patients with renal disease.

QT variability unrelated to RR variability during stress testing for identification of coronary artery disease.

Stress test electrocardiogram (ECG) analysis is widely used for coronary artery disease (CAD) diagnosis despite its limited accuracy. Alterations in autonomic modulation of cardiac electrical activity have been reported in CAD patients during acute ischemia. We hypothesized that those alterations could be reflected in changes in ventricular repolarization dynamics during stress testing that could be measured through QT interval variability (QTV). However, QTV is largely dependent on RR interval variability (RRV), which might hinder intrinsic ventricular repolarization dynamics. In this study, we investigated whether different markers accounting for low-frequency (LF) oscillations of QTV unrelated to RRV during stress testing could be used to separate patients with and without CAD. Power spectral density of QTV unrelated to RRV was obtained based on time-frequency coherence estimation. Instantaneous LF power of QTV and QTV unrelated to RRV were obtained. LF power of QTV unrelated to RRV normalized by LF power of QTV was also studied. Stress test ECG of 100 patients were analysed. Patients referred to coronary angiography were classified into non-CAD or CAD group. LF oscillations in QTV did not show significant differences between CAD and non-CAD groups. However, LF oscillations in QTV unrelated to RRV were significantly higher in the CAD group as compared with the non-CAD group when measured during the first phases of exercise and last phases of recovery. ROC analysis of these indices revealed area under the curve values ranging from 61 to 73%. Binomial logistic regression analysis revealed LF power of QTV unrelated to RRV, both during the first phase of exercise and last phase of recovery, as independent predictors of CAD. In conclusion, this study highlights the importance of removing the influence of RRV when measuring QTV during stress testing for CAD identification and supports the added value of LF oscillations of QTV unrelated to RRV to diagnose CAD from the first minutes of exercise. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove.

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study.

BACKGROUND: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([K+]) variations (Δ[K+]) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [K+] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects Δ[K+] and two time-warping-based T-wave morphological parameters, dw and its heart-rate corrected version dw,c, have been shown to reliably track Δ[K+] from the ECG. The aim of this study is to derive polynomial models relating dw and dw,c with Δ[K+], and to test their ability to reliably sense and quantify Δ[K+] values. METHODS: 48-hour Holter ECGs and [K+] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, dw and dw,c were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman's (ρ) and Pearson's (r) correlation coefficients, and the estimation error (ed) between Δ[K+] and the corresponding model-estimated values (Δ^[K+]) were calculated. RESULTS AND DISCUSSIONS: Nonlinear models were the most suitable for Δ[K+] estimation, rendering higher Pearson's correlation (median 0.77 ≤r≤ 0.92) and smaller estimation error (median 0.20 ≤ed≤ 0.43) than the linear model (median 0.76 ≤r≤ 0.86 and 0.30 ≤ed≤ 0.40), even if similar Spearman's ρ were found across models (median 0.77 ≤ρ≤ 0.83). CONCLUSION: Results support the use of nonlinear T-wave-based models as Δ[K+] sensors in ESRD-HD patients.

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity.

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.

Automatic Detection of Slow Conducting Channels during Substrate Ablation of Scar-Related Ventricular Arrhythmias.

BACKGROUND: Voltage mapping allows identifying the arrhythmogenic substrate during scar-related ventricular arrhythmia (VA) ablation procedures. Slow conducting channels (SCCs), defined by the presence of electrogram (EGM) signals with delayed components (EGM-DC), are responsible for sustaining VAs and constitute potential ablation targets. However, voltage mapping, as it is currently performed, is time-consuming, requiring a manual analysis of all EGMs to detect SCCs, and its accuracy is limited by electric far-field. We sought to evaluate an algorithm that automatically identifies EGM-DC, classifies mapping points, and creates new voltage maps, named "Slow Conducting Channel Maps" (SCC-Maps). METHODS: Retrospective analysis of electroanatomic maps (EAM) from 20 patients (10 ischemic, 10 with arrhythmogenic right ventricular dysplasia/cardiomyopathy) was performed. EAM voltage maps were acquired during sinus rhythm and used for ablation. Preprocedural contrast-enhanced cardiac magnetic resonance (Ce-CMR) imaging was available for the ischemic population. Three mapping modalities were analysed: (i) EAM voltage maps using standard (EAM standard) or manual (EAM screening) thresholds for defining core and border zones; (ii) SCC-Maps derived from the use of the novel SCC-Mapping algorithm that automatically identify EGM-DCs measuring the voltage of the local component; and (iii) Ce-CMR maps (when available). The ability of each mapping modality in identifying SCCs and their agreement was evaluated. RESULTS: SCC-Maps and EAM screening identified a greater number of SCC entrances than EAM standard (3.45 ± 1.61 and 2.95 ± 2.31, resp., vs. 1.05 ± 1.10; p < 0.01). SCC-Maps and EAM screening highly correlate with Ce-CMR maps in the ischemic population when compared to EAM standard (Lin's correlation = 0.628 and 0.679, resp., vs. 0.212, p < 0.01). CONCLUSION: The SCC-Mapping algorithm allows an operator-independent analysis of EGM signals showing better identification of the arrhythmogenic substrate characteristics when compared to standard voltage EAM.

Measuring Spin of the Remnant Black Hole from Maximum Amplitude.

Gravitational waves emitted during the merger of two black holes carry information about the remnant black hole, namely its mass and spin. This information is typically found from the ringdown radiation as the black hole settles to a final state. We find that the remnant black hole spin is already known at the peak amplitude of the gravitational wave strain. Using this knowledge, we present a new method for measuring the final spin that is template independent, using only the chirp mass, the instantaneous frequency of the strain, and its derivative at maximum amplitude, all template independent.

Tracking Black Hole Kicks from Gravitational-Wave Observations.

Coalescing binary black holes emit anisotropic gravitational radiation. This causes a net emission of linear momentum that produces a gradual acceleration of the source. As a result, the final remnant black hole acquires a characteristic velocity known as recoil velocity or gravitational kick. The symmetries of gravitational wave emission are reflected in the interactions of the gravitational wave modes emitted by the binary. In this Letter, we make use of the rich information encoded in the higher-order modes of the gravitational wave emission to infer the component of the kick along the line of sight (or radial kick). We do this by performing parameter inference on simulated signals given by numerical relativity waveforms for nonspinning binaries using numerical relativity templates of aligned-spin (nonprecessing) binary black holes. We find that for suitable sources, namely those with mass ratio q≥2 and total mass M∼100 M_{⊙}, and for modest radial kicks of 120 km/s, the 90% credible intervals of our posterior probability distributions can exclude a zero kick at a signal-to-noise ratio of 15, using a single Advanced LIGO detector working at its early sensitivity. The measurement of a nonzero radial kick component would provide the first observational signature of net transport of linear momentum by gravitational waves away from their source.

Automatic activation mapping and origin identification of idiopathic outflow tract ventricular arrhythmias.

PURPOSE: Activation mapping is used to guide ablation of idiopathic outflow tract ventricular arrhythmias (OTVAs). Isochronal activation maps help to predict the site of origin (SOO): left vs right outflow tract (OT). We evaluate an algorithm for automatic activation mapping based on the onset of the bipolar electrogram (EGM) signal for predicting the SOO and the effective ablation site in OTVAs. METHODS: Eighteen patients undergoing ablation due to idiopathic OTVAs were studied (12 with left ventricle OT origin). Right ventricle activation maps were obtained offline with an automatic algorithm and compared with manual annotation maps obtained during the intervention. Local activation time (LAT) accuracy was assessed, as well as the performance of the 10ms earliest activation site (EAS) isochronal area in predicting the SOO. RESULTS: High correlation was observed between manual and automatic LATs (Spearman's: 0.86 and Lin's: 0.85, both p<0.01). The EAS isochronal area were closely located in both map modalities (5.55 ± 3.56mm) and at a similar distance from the effective ablation site (0.15±2.08mm difference, p=0.859). The 10ms isochronal area longitudinal/perpendicular diameter ratio measured from automatic maps showed slightly superior SOO identification (67% sensitivity, 100% specificity) compared with manual maps (67% sensitivity, 83% specificity). CONCLUSIONS: Automatic activation mapping based on the bipolar EGM onset allows fast, accurate and observer-independent identification of the SOO and characterization of the spreading of the activation wavefront in OTVAs.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology.

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

Heart morphology differences induced by intrauterine growth restriction and preterm birth measured on the ECG at preadolescent age.

Intrauterine Growth Restriction (IUGR) and premature birth are associated with higher risk of cardiovascular diseases throughout adulthood. The aim of this study was to evaluate the influence of these factors in ventricular electrical remodeling in preadolescents. Electrocardiography was performed in a cohort of 33-IUGR, 32-preterm with appropriate weight and 60 controls. Depolarization and repolarization processes were studied by means of the surface ECG, including loops and angles corresponding to QRS and T-waves. The angles between the dominant vector of QRS and the frontal plane XY were different among the study groups: controls [20.03°(10.11°-28.64°)], preterm [25.48°(19.79°-33.56°)], and IUGR [27.77°(16.59°-33.23°)]. When compared to controls, IUGR subjects also presented wider angles between the difference of QRS and T-wave dominant vectors and the XY-plane [5.28°±12.15° vs 0.49°±14.15°, p<0.05] while preterm ones showed smaller frontal QRS-T angle [4.68°(2.20°-12.89°) vs 6.57°(2.72°-11.31°), p<0.05]. Thus, electrical remodeling is present in IUGR and preterm preadolescents, and might predispose them to cardiovascular diseases in adulthood. Follow-up studies are warranted.

Automatic SVM classification of sudden cardiac death and pump failure death from autonomic and repolarization ECG markers.

BACKGROUND: Considering the rates of sudden cardiac death (SCD) and pump failure death (PFD) in chronic heart failure (CHF) patients and the cost-effectiveness of their preventing treatments, identification of CHF patients at risk is an important challenge. In this work, we studied the prognostic performance of the combination of an index potentially related to dispersion of repolarization restitution (Δα), an index quantifying T-wave alternans (IAA) and the slope of heart rate turbulence (TS) for classification of SCD and PFD. METHODS: Holter ECG recordings of 597 CHF patients with sinus rhythm enrolled in the MUSIC study were analyzed and Δα, IAA and TS were obtained. A strategy was implemented using support vector machines (SVM) to classify patients in three groups: SCD victims, PFD victims and other patients (the latter including survivors and victims of non-cardiac causes). Cross-validation was used to evaluate the performance of the implemented classifier. RESULTS: Δα and IAA, dichotomized at 0.035 (dimensionless) and 3.73 µV, respectively, were the ECG markers most strongly associated with SCD, while TS, dichotomized at 2.5 ms/RR, was the index most strongly related to PFD. When separating SCD victims from the rest of patients, the individual marker with best performance was Δα≥0.035, which, for a fixed specificity (Sp) of 90%, showed a sensitivity (Se) value of 10%, while the combination of Δα and IAA increased Se to 18%. For separation of PFD victims from the rest of patients, the best individual marker was TS ≤ 2.5 ms/RR, which, for Sp=90%, showed a Se of 26%, this value being lower than Se=34%, produced by the combination of Δα and TS. Furthermore, when performing SVM classification into the three reported groups, the optimal combination of risk markers led to a maximum Sp of 79% (Se=18%) for SCD and Sp of 81% (Se=14%) for PFD. CONCLUSIONS: The results shown in this work suggest that it is possible to efficiently discriminate SCD and PFD in a population of CHF patients using ECG-derived risk markers like Δα, TS and IAA.

The STAFF III ECG database and its significance for methodological development and evaluation.

The development of new techniques for detection and characterization of transient myocardial ischemia has benefited considerably from the STAFF III database, acquired in patients receiving elective prolonged percutaneous transluminal coronary angiography. The present article reviews a range of techniques developed and/or evaluated on the ECG signals of this database, including techniques for exploring abnormal intra-QRS potentials, QRS slopes, QRS angles, T wave morphology, T wave alternans, spatiotemporal ECG information, as well as heart rate dynamics. The detection of changes in body position is also briefly reviewed as it is intimately related to ischemia detection.

QT/RR and T-peak-to-end/RR curvatures and slopes in chronic heart failure: relation to sudden cardiac death.

BACKGROUND: Previous studies investigated the QT/RR relationship by linear regressions of QT and RR intervals. However, the pattern of the QT/RR relationship is not necessarily linear. This study investigated the QT/RR and T-peak-to-end (Tpe)/RR curvatures and corresponding slopes in chronic heart failure (CHF) patients, and studied their differences between sudden cardiac death (SCD) victims and others. METHODS: Holter ECG recordings of 650 CHF patients were analyzed. RR, QT and Tpe series were obtained and for each patient, the data of each subject were fitted with a non-linear regression function of the form: QT=χ+ϕ(1-RR(γ)), where γ is the QT/RR curvature. The same regression formula was applied to the Tpe interval series. The slopes (dimensionless units) were calculated at the averaged RR intervals and at RR of 1 second. RESULTS: The median (difference between 75th and 25th percentile) of the curvature parameter was 0.226 (2.39) for QT/RR and -0.002 (3.64) for Tpe/RR in the overall sample. For the QT/RR slope, these values were 0.170 (0.12) and 0.190 (0.10) when evaluated at RR=1 and at the averaged RR, respectively, while for the Tpe/RR slope the values were 0.016 (0.04) and 0.020 (0.04), respectively. The Tpe/RR slope showed high statistical significance for separation of SCD victims and others, particularly when evaluated at the averaged RR (median values of 0.040 vs 0.020, p=0.002), but also when evaluated at RR=1 second (0.026 vs 0.015, p=0.023). Patients with values of Tpe/RR slope above 0.042 had double incidence of SCD, for the case of the slope being evaluated at RR=1 second, and triple incidence for the case of the slope being evaluated at the averaged RR. The QT/RR slope and curvature, as well as the Tpe/RR curvature, were not different in SCD victims and in others. CONCLUSIONS: Non-linear regression models based on curvature and slope characteristics, individually obtained for each patient, were used to characterize the QT/RR and Tpe/RR relationships. Steeper Tpe/RR slopes, obtained after adjusting for the curvature parameter, were associated with higher incidence of SCD. The curvature parameter itself did not show SCD predictive value.

Obstructive Sleep Apnea Characterization: A Multimodal Cross-Recurrence-Based Approach for Investigating Atrial Fibrillation.

Obstructive sleep apnea (OSA) is believed to contribute significantly to atrial fibrillation (AF) development in certain patients. Recent studies indicate a rising risk of AF with increasing OSA severity. However, the commonly used apnea-hypopnea index in clinical practice may not adequately account for the potential cardiovascular risks associated with OSA. (1) Objective: to propose and explore a novel method for assessing OSA severity considering potential connection to cardiac arrhythmias. (2) Method: the approach utilizes cross-recurrence features to characterize OSA and AF by considering the relationships among oxygen desaturation, pulse arrival time, and heart-beat intervals. Multinomial logistic regression models were trained to predict four levels of OSA severity and four groups related to heart rhythm issues. The rank biserial correlation coefficient, \boldmath rrb, was used to estimate effect size for statistical analysis. The investigation was conducted using the MESA database, which includes polysomnography data from 2055 subjects. (3) Results: a derived cross-recurrence-based index showed a significant association with a higher OSA severity (\boldmath p 0.01) and the presence of AF (\boldmath p 0.01). Additionally, the proposed index had a significantly larger effect, \boldmath rrb, than the conventional apnea-hypopnea index in differentiating increasingly severe heart rhythm issue groups: 0.14 0.06, 0.33 0.10, and 0.41 0.07. (4) Significance: the proposed method holds relevance as a supplementary diagnostic tool for assessing the authentic state of sleep apnea in clinical practice.

Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing.

BACKGROUND: Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. METHODS: The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. RESULTS: Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. CONCLUSIONS: Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.