T-wave Peak-to-End Changes Quantified by Time-Warping Predicts Ventricular Fibrillation in a Porcine Myocardial Infarction Model.

BACKGROUND: The T-peak-to-T-end ( Tpe) interval has shown potential in predicting ventricular arrhythmic risk. It is an appealing index to be measured during ischemia since it is less influenced by ST-segment changes than the early part of the T wave. A time-warping-based index, derived from a spatially transformed PCA lead, [Formula: see text], quantifying changes in the Tpe morphology, has previously demonstrated utility in tracking repolarization changes induced by a 5-minute ischemia model in humans. The value of [Formula: see text] as a predictor of ventricular fibrillation (VF) episodes is assessed in a porcine model of myocardial ischemia with ischemia maintained for 40 minutes. METHODS: From 32 pigs undergoing a coronary occlusion, pre-occlusion and occlusion ECG recordings from 10 pigs suffering a VF episode after 10 min of occlusion (Delayed VF) and 16 that did not had any episode during the recording were analyzed. The [Formula: see text] series was measured by comparing Tpe morphologies at different stages of the occlusion relative to the peak-to-end morphology of a baseline T-wave. RESULTS: During baseline, [Formula: see text] remained stationary with an intra-recording median [IQR] value of 1.60 [1.33] ms. During artery occlusion, [Formula: see text] followed a well-marked gradual increasing trend as ischemia progressed, reaching a median of 14.58 [17.72] ms. [Formula: see text] averages were significantly higher ( ) in the VF group than in the Non-VF group at time intervals 0-5, 5-10, 10-15, 15-20, 20-25 min after occlusion onset and at 10-15, 5-10 and 5-0 minutes prior to VF episode, with median values of 12.5, 18.8, 26.8, 24.0, 31.0, 18.6, 25.0 and 28.8 vs 6.3, 7.6, 8.0, 7.8, 7.8, 8.5, 7.2 and 6.0 ms, respectively. The [Formula: see text] interval was also significantly higher in the VF group at all analyzed time periods, but with a lower significance level. Pigs with maximum [Formula: see text] ≥ 20.0 ms and [Formula: see text] ≥ 85.4 ms had significantly higher risk for VF occurring in the early 5-10 minutes interval, with 90.0%/75.0% and 80.0%/69.0% sensitivity/specificity, respectively. Univariate Cox analysis yielded hazard ratios of 12.5 for [Formula: see text] vs 5.5 for [Formula: see text]. CONCLUSIONS AND SIGNIFICANCE: The time-warping-based index, [Formula: see text], is a stronger VF predictor than [Formula: see text] during ischemia in a porcine model, advising for further clinical exploration studies in humans.

Tracking Tidal Volume from Holter and Wearable Armband Electrocardiogram Monitoring.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Persistent sleep-disordered breathing independently contributes to metabolic syndrome in prepubertal children.

BACKGROUND: Obstructive sleep apnea (OSA) is a risk factor for metabolic syndrome (MetS) in adults, but its association in prepubertal children is still questionable due to the relatively limited cardiometabolic data available and the phenotypic heterogeneity. OBJECTIVE: To identify the role of OSA as a potential mediator of MetS in prepubertal children. METHODS: A total of 255 prepubertal children from the Childhood Adenotonsillectomy Trial were included, with standardized measurements taken before OSA treatment and 7 months later. MetS was defined if three or more of the following criteria were present: adiposity, high blood pressure, elevated glycemia, and dyslipidemia. A causal mediation analysis was conducted to assess the effect of OSA treatment on MetS. RESULTS: OSA treatment significantly impacted MetS, with the apnea-hypopnea index emerging as mediator (p = .02). This mediation role was not detected for any of the individual risk factors that define MetS. We further found that the relationship between MetS and OSA is ascribable to respiratory disturbance caused by the apnea episodes, while systemic inflammation as measured by C-reactive protein, is mediated by desaturation events and fragmented sleep. In terms of evolution, patients with MetS were significantly more likely to recover after OSA treatment (odds ratio = 2.56, 95% confidence interval [CI] 1.20-5.46; risk ratio = 2.06, 95% CI 1.19-3.54) than the opposite, patients without MetS to develop it. CONCLUSION: The findings point to a causal role of OSA in the development of metabolic dysfunction, suggesting that persistent OSA may increase the risk of MetS in prepubertal children. This mediation role implies a need for developing screening for MetS in children presenting OSA symptoms.

A robophysical model of spacetime dynamics.

Systems consisting of spheres rolling on elastic membranes have been used to introduce a core conceptual idea of General Relativity: how curvature guides the movement of matter. However, such schemes cannot accurately represent relativistic dynamics in the laboratory because of the dominance of dissipation and external gravitational fields. Here we demonstrate that an "active" object (a wheeled robot), which moves in a straight line on level ground and can alter its speed depending on the curvature of the deformable terrain it moves on, can exactly capture dynamics in curved relativistic spacetimes. Via the systematic study of the robot's dynamics in the radial and orbital directions, we develop a mapping of the emergent trajectories of a wheeled vehicle on a spandex membrane to the motion in a curved spacetime. Our mapping demonstrates how the driven robot's dynamics mix space and time in a metric, and shows how active particles do not necessarily follow geodesics in the real space but instead follow geodesics in a fiducial spacetime. The mapping further reveals how parameters such as the membrane elasticity and instantaneous speed allow the programming of a desired spacetime, such as the Schwarzschild metric near a non-rotating blackhole. Our mapping and framework facilitate creation of a robophysical analog to a general relativistic system in the laboratory at low cost that can provide insights into active matter in deformable environments and robot exploration in complex landscapes.

Model-based Estimators of QT Series Time Delay in Following Heart-Rate Changes.

Sudden cardiac death is the leading cause of death among cardiovascular diseases. Markers for patient risk stratification focusing on QT-interval dynamics in response to heart-rate (HR) changes can be characterized in terms of parametric QT to RR dependence and QT/RR hysteresis. The QT/RR hysteresis can be quantified by the time delay the QT interval takes to accommodate for the HR changes. The exercise stress test has been proposed as a proper test, with large HR dynamics, to evaluate the QT/RR hysteresis. The present study aims at evaluating several time-delay estimators based on noise statistic (Gaussian or Laplacian) and HR changes profile at stress test (gradual transition change). The estimator's performance was assessed on a simulated QT transition contaminated by noise and in a clinical study including patients affected by coronary arteries disease (CAD). As expected, the Laplacian and Gaussian estimators yield the best results when noise follows the respective distribution. Further, the Laplacian estimator showed greater discriminative power in classifying different levels of cardiac risk in CAD patients, suggesting that real data fit better the Laplacian distribution than the Gaussian one. The Laplacian estimator appears to be the choice for time-delay estimation of QT/RR hysteresis lag in response to HR changes in stress test.Clinical Relevance-The proposed time-delay estimator of QT/RR hysteresis lag improves its significance as biomarkers for coronary artery diseases risk stratification.

Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations.

Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p 0.001) and multiclass (r=0.91, p 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home.

Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium.

Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability. Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability. Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells. Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.

Time-Warping Analysis of the T-Wave Peak-to-End Interval to Quantify Ventricular Repolarization Dispersion During Ischemia.

Variations in the dispersion of ventricular repolarization can be quantified by T-wave time-warping based index, dw. However, the early phase of the T-wave can be affected by ST-segment changes during ischemia. We hypothesized that restricting dw to the T-wave peak-to-end ( Tpe) would circumvent this limitation while still quantifying variations in repolarization dispersion. A total of 101 ECG recordings from patients undergoing coronary occlusion, together with their control recordings, were analyzed. A series of dw values was calculated by quantifying the Tpe morphological variations between the T-waves at different occlusion stages and a baseline T-wave. We introduced a normalized version of dw, Rd, reflecting variations of dw during occlusion relative to control recordings ( Rd = 1 corresponds to the same level of variation). The dw series followed a gradually increasing trend with occlusion time, reaching median [range] Rd values of 9.44 [1.01, 80.74] at the occlusion end. Rd at occlusion end was significantly higher than threshold values of 1, 2, 5, and 10 in 94.1%, 85.11%, 64.4% and 48.5% of patients, respectively. The spatial lead-wise analysis of dw showed distinct distributions depending on the occluded artery, suggesting a relation with the ischemia location. The relative variation R with ischemia of index dw (9.4) is greater than that of the T-wave amplitude (7.7), Tpe interval (2.7) and T-wave width (3.0). In conclusion, dw tracks ischemic-induced variations in repolarization dispersion in a more robust manner than classical indexes, avoiding the impact of ST segment elevation/depression or early T-wave distortions, thus warranting further clinical studies.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

QRS-T Angles as Markers for Heart Sphericity in Subjects With Intrauterine Growth Restriction: A Simulation Study.

Changes induced by intrauterine growth restriction (IUGR) in cardiovascular anatomy and function that persist throughout life have been associated with a higher predisposition to heart disease in adulthood. Together with cardiac morphological remodelling, evaluated through the ventricular sphericity index, alterations in cardiac electrical function have been reported by characterization of the depolarization and repolarization loops, and their angular relationship, measured from the vectorcardiogram. The underlying relationship between the morphological remodelling and the angular variation of QRS and T-wave dominant vectors, if any, has not been explored. The aim of this study was to evaluate this relationship using computational models based on realistic heart and torso in which IUGR-induced morphological changes were incorporated by reducing the ventricular sphericity index. Specifically, we departed from a control model and we built eight different globular heart models by reducing the base-to-apex length and enlarging the basal ventricular diameter. We computed QRS and T-wave dominant vectors and angles from simulated pseudo-electrocardiograms and we compared them with clinical measurements. Results for the QRS to T angles follow a change trend congruent with that reported in clinical data, supporting the hypothesis that the IUGR-induced morphological remodelling could contribute to explain the observed angle changes in IUGR patients. By additionally varying the position of the ventricles with respect to the torso and the electrodes, we found that electrode displacement can impact the quantified angles and should be considered when interpreting the results.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions.

The present article proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.

Steady-state and transient effects of SK channel block and adrenergic stimulation to counteract acetylcholine-induced arrhythmogenic effects in the human atria: A computational study.

Hyperactivity of the parasympathetic nervous system has been linked to the development of paroxysmal atrial fibrillation (AF). The parasympathetic neurotransmitter acetylcholine (ACh) causes a reduction in action potential (AP) duration (APD) and an increase in resting membrane potential (RMP), both of which contribute to enhance the risk for reentry. Research suggests that small-conductance calcium activated potassium (SK) channels may be an effective target for treating AF. Therapies targeting the autonomic nervous system, either alone or in combination with other drugs, have been explored and have been shown to decrease the incidence of atrial arrhythmias. This study uses computational modeling and simulation to examine the impact of SK channel block (SKb) and ß-adrenergic stimulation through Isoproterenol (Iso) on countering the negative effects of cholinergic activity in human atrial cell and 2D tissue models. The steady-state effects of Iso and/or SKb on AP shape, APD at 90% repolarization (APD90) and RMP were evaluated. The ability to terminate stable rotational activity in cholinergically-stimulated 2D tissue models of AF was also investigated. A range of SKb and Iso application kinetics, which reflect varying drug binding rates, were taken into consideration. The results showed that SKb alone prolonged APD90 and was able to stop sustained rotors in the presence of ACh concentrations up to 0.01 µM. Iso terminated rotors under all tested ACh concentrations, but resulted in highly-variable steady-state outcomes depending on baseline AP morphology. Importantly, the combination of SKb and Iso resulted in greater APD90 prolongation and showed promising anti-arrhythmic potential by stopping stable rotors and preventing re-inducibility.

The role of ß-adrenergic stimulation in QT interval adaptation to heart rate during stress test.

The adaptation lag of the QT interval after heart rate (HR) has been proposed as an arrhythmic risk marker. Most studies have quantified the QT adaptation lag in response to abrupt, step-like changes in HR induced by atrial pacing, in response to tilt test or during ambulatory recordings. Recent studies have introduced novel methods to quantify the QT adaptation lag to gradual, ramp-like HR changes in stress tests by evaluating the differences between the measured QT series and an estimated, memoryless QT series obtained from the instantaneous HR. These studies have observed the QT adaptation lag to progressively reduce when approaching the stress peak, with the underlying mechanisms being still unclear. This study analyzes the contribution of ß-adrenergic stimulation to QT interval rate adaptation in response to gradual, ramp-like HR changes. We first quantify the QT adaptation lag in Coronary Artery Disease (CAD) patients undergoing stress test. To uncover the involved mechanisms, we use biophysically detailed computational models coupling descriptions of human ventricular electrophysiology and ß-adrenergic signaling, from which we simulate ventricular action potentials and ECG signals. We characterize the adaptation of the simulated QT interval in response to the HR time series measured from each of the analyzed CAD patients. We show that, when the simulated ventricular tissue is subjected to a time-varying ß-adrenergic stimulation pattern, with higher stimulation levels close to the stress peak, the simulated QT interval presents adaptation lags during exercise that are more similar to those measured from the patients than when subjected to constant ß-adrenergic stimulation. During stress test recovery, constant and time-varying ß-adrenergic stimulation patterns render similar adaptation lags, which are generally shorter than during exercise, in agreement with results from the patients. In conclusion, our findings support the role of time-varying ß-adrenergic stimulation in contributing to QT interval adaptation to gradually increasing HR changes as those seen during the exercise phase of a stress test.

Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability.

Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1-5 e/s; 5-10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.

The frequency of atrial fibrillatory waves is modulated by the spatiotemporal pattern of acetylcholine release: a 3D computational study.

In atrial fibrillation (AF), the ECG P-wave, which represents atrial depolarization, is replaced with chaotic and irregular fibrillation waves (f waves). The f-wave frequency, F f, shows significant variations over time. Cardiorespiratory interactions regulated by the autonomic nervous system have been suggested to play a role in such variations. We conducted a simulation study to test whether the spatiotemporal release pattern of the parasympathetic neurotransmitter acetylcholine (ACh) modulates the frequency of atrial reentrant circuits. Understanding parasympathetic involvement in AF may guide more effective treatment approaches and could help to design autonomic markers alternative to heart rate variability (HRV), which is not available in AF patients. 2D tissue and 3D whole-atria models of human atrial electrophysiology in persistent AF were built. Different ACh release percentages (8% and 30%) and spatial ACh release patterns, including spatially random release and release from ganglionated plexi (GPs) and associated nerves, were considered. The temporal pattern of ACh release, ACh(t), was simulated following a sinusoidal waveform of frequency 0.125 Hz to represent the respiratory frequency. Different mean concentrations (ACh¯) and peak-to-peak ranges of ACh (ΔACh) were tested. We found that temporal variations in F f, F f(t), followed the simulated temporal ACh(t) pattern in all cases. The temporal mean of F f(t), F¯f, depended on the fibrillatory pattern (number and location of rotors), the percentage of ACh release nodes and ACh¯. The magnitude of F f(t) modulation, ΔF f, depended on the percentage of ACh release nodes and ΔACh. The spatial pattern of ACh release did not have an impact on F¯f and only a mild impact on ΔF f. The f-wave frequency, being indicative of vagal activity, has the potential to drive autonomic-based therapeutic actions and could replace HRV markers not quantifiable from AF patients.

Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors.

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time-frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.

Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays.

Atrial fibrosis plays a key role in the initiation and progression of atrial fibrillation (AF). Atrial fibrosis is typically identified by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets. Nevertheless, this approach disregards signal spatiotemporal information and b-EGM sensitivity to catheter orientation. To overcome these limitations, we propose the dominant-to-remaining eigenvalue dominance ratio (EIGDR) of unipolar electrograms (u-EGMs) within neighbor electrode cliques as a waveform dispersion measure, hypothesizing that it is correlated with the presence of fibrosis. A simulated 2D tissue with a fibrosis patch was used for validation. We computed EIGDR maps from both original and time-aligned u-EGMs, denoted as [Formula: see text] and [Formula: see text], respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, [Formula: see text]. The performance of each map in detecting fibrosis was evaluated in scenarios including noise and variable electrode-tissue distance. Best results were achieved by [Formula: see text], reaching 94% detection accuracy, versus the 86% of b-EGMs voltage maps. The proposed strategy was also tested in real u-EGMs from fibrotic and non-fibrotic areas over 3D electroanatomical maps, supporting the ability of the EIGDRs as fibrosis markers, encouraging further studies to confirm their translation to clinical settings. Upper panels: map of [Formula: see text] from 3×3 cliques for Ψ= 0∘ and bipolar voltage map Vb-m, performed assuming a variable electrode-to-tissue distance and noisy u-EGMs (noise level σv = 46.4 µV ). Lower panels: detected fibrotic areas (brown), using the thresholds that maximize detection accuracy of each map.

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.

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.