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Objective: Phase rectified signal averaging (PRSA) is a new method of fetal heart rate variability (fHRV) analysis that quantifies the average acceleration (AC) and deceleration capacity (DC) of the heart. The aim of this study was to evaluate AC and DC of fHR [recorded by trans-abdominal fetal electrocardiogram (ta-fECG)] in relation to Doppler velocimetry characteristics of intrauterine growth restriction (IUGR).Design: Prospective case-control study.Setting: Single third referral centre.Population: IUGR (n = 66) between 25 and 40 gestational weeks and uncomplicated pregnancies (n = 79).Methods: In IUGR the nearest ta-fECG monitoring to delivery was used for PRSA analysis and Doppler velocimetry parameters obtained within 48 hours. AC and DC were computed at s = T = 9. The relation was evaluated between either AC or DC and Doppler velocimetry parameters adjusting for gestational age at monitoring, as well as the association between either AC or DC and IUGR with or without brain sparing.Results: In IUGRs there was a significant association between either AC and DC and middle cerebral artery pulsatility index (PI; P = 0.01; P = 0.005), but the same was not true for uterine or umbilical artery PI (P > 0.05). Both IUGR fetuses with and without brain sparing had lower AC and DC than controls, but this association was stronger for IUGRs with brain sparing.Conclusions: Our study observed for the first time that AC and DC at PRSA analysis are associated with middle cerebral artery PI, but not with uterine or umbilical artery PI, and that there is a significant decrease of AC and DC in association with brain sparing in IUGR fetuses from 25 weeks of gestation to term.Tweetable Abstract: Brain sparing in IUGR fetuses is associated with decreased acceleration and deceleration capacities of the heart. [ABSTRACT FROM AUTHOR]

AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

Background and Objective: Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT., Methods: The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine., Results: The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time., Conclusions: The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023. Published by Elsevier B.V.)

Determining the potential cardiotoxicity and pro-arrhythmic effects of drug candidates remains one of the most relevant issues in the drug development pipeline (DDP). New methods enabling to perform more representative preclinical in vitro studies by exploiting induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) are under investigation to increase the translational power of the outcomes. Here we present a pharmacological campaign conducted to evaluate the drug-induced QT alterations and arrhythmic events on uHeart, a 3D miniaturized in vitro model of human myocardium encompassing iPSC-CM and dermal fibroblasts embedded in fibrin. uHeart was mechanically trained resulting in synchronously beating cardiac microtissues in 1 week, characterized by a clear field potential (FP) signal that was recorded by means of an integrated electrical system. A drug screening protocol compliant with the new International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines was established and uHeart was employed for testing the effect of 11 compounds acting on single or multiple cardiac ion channels and well-known to elicit QT prolongation or arrhythmic events in clinics. The alterations of uHeart's electrophysiological parameters such as the beating period, the FP duration, the FP amplitude, and the detection of arrhythmic events prior and after drug administration at incremental doses were effectively analyzed through a custom-developed algorithm. Results demonstrated the ability of uHeart to successfully anticipate clinical outcome and to predict the QT prolongation with a sensitivity of 83.3%, a specificity of 100% and an accuracy of 91.6%. Cardiotoxic concentrations of drugs were notably detected in the range of the clinical highest blood drug concentration (Cmax), qualifying uHeart as a fit-to-purpose preclinical tool for cardiotoxicity studies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Toxicology.)

Objective: T-wave alternans (TWA) manifests as beat-to-beat fluctuations of T-wave morphology on the electrocardiogram (ECG), with physiological bases not fully understood. Using a biophysical model of the ECG, we demonstrate and give explicit relations that TWA depends on the i) spatial covariance between myocytes' repolarization time and alternans; and ii) global alternans (common to every myocyte)., Methods: We quantified the spatial covariance and global alternans by means of two new metrics, R index and δ, respectively. They were validated on both synthetic and real signals. Computerized simulations were generated using a biophysical model linking the action potentials with the surface ECG. Then, the metrics were computed in STAFF-III dataset, containing ECGs from patients who underwent coronary angioplasty with prolonged balloon inflations, and the time courses of the metrics were analyzed together with TWA measured on the surface ECG., Results: The metrics properly estimated the spatial covariance and global alternans in the synthetic data. In the STAFF-III dataset, the R index progressively increased from baseline to the fourth minute of inflation (median ∆R=0.81 ms; p 0.05), whereas δ was mostly unaltered during the intervention ( δ=0 ms)., Conclusion: We reported, for the first time, that TWA is significantly driven by the myocyte's spatial covariance between their repolarization times and alternans, and not by global alternans, when TWA is generated by regional ischemia., Significance: The metrics may reveal new complementary insights into the mechanisms underlying TWA.

Aims: Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG)., Methods and Results: Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients-three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%., Conclusion: Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Deep Learning approaches are powerful tools in a great variety of classification tasks. However, they are limitedly accepted or trusted in clinical frameworks due to their typical "black box" outline: their architecture is well-known, but processes employed in classification are often inaccessible to humans. With this work, we explored the problem of "Explainable AI" (XAI) in Alzheimer's disease (AD) classification tasks. Data from a neuroimaging cohort (n = 251 from OASIS-3) of early-stage AD dementia and healthy controls (HC) were analysed. The MR scans were initially fed to a pre-trained DL model, which achieved good performance on the test set (AUC: 0.82, TPR: 0.78, TNR: 0.81). Results were then investigated by means of an XAI approach (Occlusion Sensitivity method) that provided measures of relevance (RV) as outcome. We compared RV values obtained within healthy tissues with those underlying white matter hyperintensity (WMH) lesions. The analysis was conducted on 4 different groups of data, obtained by stratifying correct and misclassified images according to the health condition of participants (AD/HC). Results highlighted that the DL model found favourable leveraging lesioned brain areas for AD identification. A statistically significant difference ( ) between WMH and healthy tissue contributions was indeed observed for AD recognition, differently from the HC case ( p=0.27). Clinical Relevance - This study, though preliminary, suggested that DL models might be trained to use known clinical information and reinforced the role of WMHs as neuroimaging biomarker for AD dementia. The outlined findings have a significant clinical relevance as they prepare the ground for a progressive increase in the level of trust laid in DL approaches.

Background: The effect of the ventricular repolarization heterogeneity has not been systematically assessed in patients with atrial fibrillation (AF). Aim of this study is to assess ventricular repolarization heterogeneity as predictor of cardiovascular (CV) death and/or other CV events in patients with AF., Methods: From the multicenter prospective Swiss-AF (Swiss Atrial Fibrillation) Cohort Study, we enrolled 1711 patients who were in sinus rhythm (995) or AF (716). Resting ECG recordings of 5-min duration were obtained at baseline. Parameters assessing ventricular repolarization were computed (QTc, Tpeak-Tend, J-Tpeak and V-index)., Results: During AF, the V-index was found repeatable (no differences when computed over the whole recording, on the first 2.5-min and on the last 2.5-min segments). During a mean follow-up time of 2.6 ± 1.0 years, 90 patients died for CV reasons. In bivariate Cox regression analysis (adjusted for age only), the V-index was associated with an increased risk of CV death, both in the subgroup of patients in sinus rhythm (SR) as well as those in AF. In multivariate analysis adjusted for clinical risk factors and medications, both prolonged QTc and V-index were independently associated with an increased risk of CV death (QTc: hazard ratio [HR] 2.78, 95% CI 1.79-4.32, p < 0.001; V-index: HR 1.73, 95% CI 1.12-2.69, p = 0.014)., Conclusions: QTc and V-index, measured in a single 5-min ECG recording, were independent predictors of CV death in a cohort of patients with AF and might be a valuable tool for further risk stratification to guide patient management. Clinical Trial Identifier Swiss-AF study: NCT02105844., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Aims: Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF., Methods: A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments., Results: The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified., Conclusion: Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Luongo, Rees, Nairn, Rivolta, Dössel, Sassi, Ahlgrim, Mayer, Neumann, Arentz, Jadidi, Loewe and Müller-Edenborn.)

Recent studies have suggested that cardiac abnormalities can be detected from the electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL algorithms lack interpretability, since they do not provide any justification for their decisions. In this study, we designed two new frameworks to interpret the classification results of DL algorithms trained for 12-lead ECG classification. The frameworks allow us to highlight not only the ECG samples that contributed most to the classification, but also which between the P-wave, QRS complex and T-wave, hereafter simply called 'waves', were the most relevant for the diagnosis. The frameworks were designed to be compatible with any DL model, including the ones already trained. The frameworks were tested on a selected Deep Neural Network, trained on a publicly available dataset, to automatically classify 24 cardiac abnormalities from 12-lead ECG signals. Experimental results showed that the frameworks were able to detect the most relevant ECG waves contributing to the classification. Often the network relied on portions of the ECG which are also considered by cardiologists to detect the same cardiac abnormalities, but this was not always the case. In conclusion, the proposed frameworks may unveil whether the network relies on features which are clinically significant for the detection of cardiac abnormalities from 12-lead ECG signals, thus increasing the trust in the DL models. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

During labor, uterine contractions trigger the response of the autonomic nervous system (ANS) of the fetus, producing sawtooth-like decelerations in the fetal heart rate (FHR) series. Under chronic hypoxia, ANS is known to regulate FHR differently with respect to healthy fetuses. In this study, we hypothesized that such different ANS regulation might also lead to a change in the FHR deceleration morphology. The hypothesis was tested in an animal model comprising nine normoxic and five chronically hypoxic fetuses that underwent a protocol of umbilical cord occlusions (UCOs). Deceleration morphologies in the fetal inter-beat time interval (FRR) series were modeled using a trapezoid with four parameters, i.e., baseline b , deceleration depth a , UCO response time τ u and recovery time τ r . Comparing normoxic and hypoxic sheep, we found a clear difference for τ u (24.8±9.4 vs. 39.8±9.7 s; p < 0.05), a (268.1±109.5 vs. 373.0±46.0 ms; p < 0.1) and Δτ = τ u - τ r (13.2±6.9 vs. 23.9±7.5 s; p < 0.05). Therefore, the animal model supported the hypothesis that hypoxic fetuses have a longer response time τ u and larger asymmetry Δτ as a response to UCOs. Assessing these morphological parameters during labor is challenging due to non-stationarity, phase desynchronization and noise. For this reason, in the second part of the study, we quantified whether acceleration capacity (AC), deceleration capacity (DC), and deceleration reserve (DR), computed through Phase-Rectified Signal Averaging (PRSA, known to be robust to noise), were correlated with the morphological parameters. DC, AC and DR were correlated with τ u , τ r and Δτ for a wide range of the PRSA parameter T (Pearson's correlation ρ > 0.8, p < 0.05). In conclusion, deceleration morphologies have been found to differ between normoxic and hypoxic sheep fetuses during UCOs. The same difference can be assessed through PRSA based parameters, further motivating future investigations on the translational potential of this methodology on human data., Competing Interests: MF has a patent pending on abdominal ECG signal separation for FHR monitoring (WO2018160890). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Rivolta, Barbieri, Stampalija, Sassi and Frasch.)

Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Vila, Rivolta, Luongo, Unger, Luik, Gigli, Lombardi, Loewe and Sassi.)

Aims : Bubble entropy (bEn) is an entropy metric with a limited dependence on parameters. bEn does not directly quantify the conditional entropy of the series, but it assesses the change in entropy of the ordering of portions of its samples of length m , when adding an extra element. The analytical formulation of bEn for autoregressive (AR) processes shows that, for this class of processes, the relation between the first autocorrelation coefficient and bEn changes for odd and even values of m . While this is not an issue, per se, it triggered ideas for further investigation. Methods : Using theoretical considerations on the expected values for AR processes, we examined a two-steps-ahead estimator of bEn, which considered the cost of ordering two additional samples. We first compared it with the original bEn estimator on a simulated series. Then, we tested it on real heart rate variability (HRV) data. Results : The experiments showed that both examined alternatives showed comparable discriminating power. However, for values of 10

Background: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers., Objectives: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data., Methods: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources)., Results: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class., Conclusion: Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI., (© 2021 Heart Rhythm Society.)

Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.

Background and Objectives: In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-à-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG., Methods: The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time., Results: Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p < 0.05)., Conclusions: High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity., Competing Interests: Declaration of Competing Interest The authors have no conflicts to disclose., (Copyright © 2020. Published by Elsevier B.V.)

Objective: The interpretation of Average Acceleration and Deceleration Capacities (AC/DC), computed through Phase-Rectified Signal Averaging (PRSA), in intrapartum fetal heart rate (FHR) monitoring is still matter of investigation. We aimed to elucidate some behaviors of AC/DC., Methods: We derived the theoretical value of PRSA for stationary stochastic Gaussian processes and proved that for these time series AC and DC are necessarily identical in absolute value. The difference between DC and AC, termed Deceleration Reserve (DR), was introduced to detect signal's asymmetric trends. DR was tested on FHR signals from: near-term pregnant sheep model of labor consisting of chronically hypoxic and normoxic fetuses with both groups developing acidemia due to umbilical cord occlusions (UCO); and the CTU-UHB dataset containing fetal CTG recordings collected during labor of newborns that resulted acidotic and non-acidotic, respectively. DR was compared with AC and DC in terms of discriminatory power (AUC), between the groups, after correcting for signal power or deceleration area, respectively., Results: DR displayed higher discriminatory power on the animal model during severe acidemia, with respect to AC/DC ( ) but also distinguished correctly all chronically hypoxic from normoxic fetuses at baseline prior to UCO. DR also outperformed AC/DC on the CTU-UHB dataset in distinguishing acidemic fetuses at birth (AUC: 0.65)., Conclusion: Theoretical results motivated the introduction of DR, that proved to be superior than AC/DC for risk stratification during labor., Significance: DR, measured during labor, might permit to distinguish acidemic fetuses due to their different autonomic regulation, paving the way for new monitoring strategies.

Background: Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls., Methods: In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty., Results: Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD., Limitations: The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected., Conclusions: The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p <; 0.05). On real data, our methodology outperformed two variants of ABS (p <; 0.05) and performed similarly to interpolation when considering the high power residues left (both <; 5%), and the log likelihood with the fitted AR model.

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respectively) in combination with HRV for automatic sleep vs wake classification. Accelerometer and ECG signals were collected during polysomnographic studies (PSGs) including 18 individuals (25-53 years old) with no previous history of sleep disorders. Then, an experienced neurologist performed sleep staging on PSG data. Eleven features from HRV and accelerometry were extracted from series of different lengths. A support vector machine (SVM) was used to automatically distinguish sleep and wake. We found 7 min as the optimal signal length for classification, while maximizing specificity (wake detection). CACT and WACT provided similar accuracies (78% chest vs 77% wrist), larger than what yielded by HRV alone (66%). The addition of HRV to CACT reduced slightly the accuracy, while improving specificity (from 33% to 51%, p < 0.05). On the contrary, the concurrent usage of HRV and WACT did not provide statistically significant improvements over WACT. Then, a subset of features (3 from HRV + 1 from actigraphy) was selected by reducing redundancy using a strategy based on Spearman's correlation and area under the ROC curve. The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features. The study opens interesting possibilities in the design of wearable devices for long-term monitoring of sleep at home., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Falling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score ≤ 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R 2 : 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set.

Background: The V-index is an ECG marker quantifying spatial heterogeneity of ventricular repolarization. We prospectively assessed the diagnostic and prognostic values of the V-index in patients with suspected non-ST-elevation myocardial infarction (NSTEMI)., Methods: We prospectively enrolled 497 patients presenting with suspected NSTEMI to the emergency department (ED). Digital 12-lead ECGs of five-minute duration were recorded at presentation. The V-index was automatically calculated in a blinded fashion. Patients with a QRS duration >120ms were ruled out from analysis. The final diagnosis was adjudicated by two independent cardiologists. The prognostic endpoint was all-cause mortality during 24months of follow-up., Results: NSTEMI was the final diagnosis in 14% of patients. V-index levels were higher in patients with AMI compared to other causes of chest pain (median 23ms vs. 18ms, p<0.001). The use of the V-index in addition to conventional ECG-criteria improved the diagnostic accuracy for the diagnosis of NSTEMI as quantified by area under the ROC curve from 0.66 to 0.73 (p=0.001) and the sensitivity of the ECG for AMI from 41% to 86% (p<0.001). Cumulative 24-month mortality rates were 99.4%, 98.4% and 88.3% according to tertiles of the V-index (p<0.001). After adjustment for age and important ECG and clinical parameters, the V-index remained an independent predictor of death., Conclusions: The V-index, an ECG marker quantifying spatial heterogeneity of ventricular repolarization, significantly improves the accuracy and sensitivity of the ECG for the diagnosis of NSTEMI and independently predicts mortality during follow-up., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: The increase in life expectancy is accompanied by a growing number of elderly subjects affected by chronic comorbidities, a health issue which also implies important socioeconomic consequences. Shifting from hospital or community dwelling care towards a home personalized healthcare paradigm would promote active aging with a better quality of life, along with a reduction in healthcare-related costs., Objective: The aim of the SMARTA project was to develop and test an innovative personal health system integrating standard sensors as well as innovative wearable and environmental sensors to allow home telemonitoring of vital parameters and detection of anomalies in daily activities, thus supporting active aging through remote healthcare., Methods: A first phase of the project consisted in the definition of the health and environmental parameters to be monitored (electrocardiography and actigraphy, blood pressure and oxygen saturation, weight, ear temperature, glycemia, home interaction monitoring - water tap, refrigerator, and dishwasher), the feedbacks for the clinicians, and the reminders for the patients. It was followed by a technical feasibility analysis leading to an iterative process of prototype development, sensor integration, and testing. Once the prototype had reached an advanced stage of development, a group of 32 volunteers - including 15 healthy adult subjects, 13 elderly people with cardiac diseases, and 4 clinical operators - was recruited to test the system in a real home setting, in order to evaluate both technical reliability and user perception of the system in terms of effectiveness, usability, acceptance, and attractiveness., Results: The testing in a real home setting showed a good perception of the SMARTA system and its functionalities both by the patients and by the clinicians, who appreciated the user interface and the clinical governance system. The moderate system reliability of 65-70% evidenced some technical issues, mainly related to sensor integration, while the patient's user interface showed excellent reliability (100%)., Conclusions: Both elderly people and clinical operators considered the SMARTA system a promising and attractive tool for improving patients' healthcare while reducing related costs and preserving quality of life. However, the moderate reliability of the system should prompt further technical developments in terms of sensor integration and usability of the clinical operator's user interface., (© 2017 S. Karger AG, Basel.)

Objective: Phase rectified signal averaging (PRSA) is a new method of fetal heart rate variability (fHRV) analysis that quantifies the average acceleration (AC) and deceleration capacity (DC) of the heart. The aim of this study was to evaluate AC and DC of fHR [recorded by trans-abdominal fetal electrocardiogram (ta-fECG)] in relation to Doppler velocimetry characteristics of intrauterine growth restriction (IUGR)., Design: Prospective case-control study., Setting: Single third referral centre., Population: IUGR (n = 66) between 25 and 40 gestational weeks and uncomplicated pregnancies (n = 79)., Methods: In IUGR the nearest ta-fECG monitoring to delivery was used for PRSA analysis and Doppler velocimetry parameters obtained within 48 hours. AC and DC were computed at s = T = 9. The relation was evaluated between either AC or DC and Doppler velocimetry parameters adjusting for gestational age at monitoring, as well as the association between either AC or DC and IUGR with or without brain sparing., Results: In IUGRs there was a significant association between either AC and DC and middle cerebral artery pulsatility index (PI; P = 0.01; P = 0.005), but the same was not true for uterine or umbilical artery PI (P > 0.05). Both IUGR fetuses with and without brain sparing had lower AC and DC than controls, but this association was stronger for IUGRs with brain sparing., Conclusions: Our study observed for the first time that AC and DC at PRSA analysis are associated with middle cerebral artery PI, but not with uterine or umbilical artery PI, and that there is a significant decrease of AC and DC in association with brain sparing in IUGR fetuses from 25 weeks of gestation to term., Tweetable Abstract: Brain sparing in IUGR fetuses is associated with decreased acceleration and deceleration capacities of the heart., (© 2015 Royal College of Obstetricians and Gynaecologists.)

Objective: Intrauterine growth restriction (IUGR) is characterized by chronic nutrient deprivation and hypoxemia that alters the autonomous nervous system regulation of fetal heart rate variability (fHRV). Phase-rectified signal averaging (PRSA) is a new algorithm capable to identify periodic and quasi-periodic patterns of HR, and which is used to quantify the average acceleration and deceleration capacity (AC/DC) of the heart. The computation of AC/DC depends on the parameters T and s, which we set so that s=T. T and s determine the periodicities that can be detected (the larger T the smaller the frequency of oscillations for which the method is most sensitive). The aim of the study was to evaluate the influence of the parameter T on PRSA computation, based on trans-abdominally acquired fetal ECG (ta-fECG), in early IUGR (<34 weeks of gestation) at two different gestational age epochs., Study Design: AC/DC were calculated for different T values (1÷45) on fetal RR intervals derived from ta-fECG in 22 IUGR and in 37 appropriate for gestational age (AGA) fetuses matched for gestational age, in two gestational age epochs: very preterm group (≥26÷<30 weeks), and preterm group (≥30÷<34 weeks), respectively., Results: AC/DC were significantly lower in IUGR than in AGA fetuses for all T≥5 values (p<0.05). The best area under the receiver operating characteristic curve (AUC) in identifying IUGR at time of recording was observed for T9 [AUC AC-T9 0.87, 95% confidence interval (CI) 0.77-0.96; and AUC DC-T9 0.89, 95% CI 0.81-0.98), and in range of T 7÷15. In the same T interval, AC/DC were significantly lower in very preterm than in preterm IUGR group (p<0.05), while there were no differences in AGA fetuses at two gestational age epochs (p>0.05), respectively. The AUCs of AC-T9 and DC-T9 significantly outperformed that obtained by short-term variation (AUC 0.77, 95% CI 0.65-0.90; p=0.009 and p=0.003, respectively)., Conclusions: Our study shows that within the range of T parameter 1÷45, T=9 proved to be the best value to discriminate the AC and DC of the fetal heart rate of IUGR from AGA fetuses prior to 34 weeks of gestation. These significant differences are emphasized in very preterm gestational age epochs., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Drug-induced alterations of ventricular heterogeneity must be limited to avoid induction of lethal ventricular arrhythmias. In here, a new parameter called [Formula: see text]-index, able to measure the standard deviation of myocites' repolarization times, was evaluated after moxifloxacin and sotalol administration. The two drugs are known to provide different alteration of the QT interval length ranging from subtle (moxifloxacin) to evident (sotalol). In fact, while the former is employed as active-comparator in thorough QT studies, the latter might induce torsades de pointes. 24 h Holter ECGs of 39 (sotalol) and 68 (moxifloxacin) healthy subjects were retrospectively analyzed. The recordings were performed after infusion of the drugs and after the placebo (moxifloxacin) or at baseline (sotalol). The corrected QT interval (QTc) was included as well in the study, for a direct comparison. In both populations, [Formula: see text]-index and QTc increased along with the drugs' serum concentration and were statistically different from values in the placebo arm or at baseline (p < 0.05).With sotalol, the maximum value of [Formula: see text]-index occurred, on average, after 5.64 h from the infusion, whereas for QTc after about 4.27 h. The two metrics displayed evident changes ([Formula: see text]-index: 27.79 ms ± 4.89 ms versus 60.13 ms ± 18.52 ms; QT corrected: 387.07 ms ± 19.84 ms versus 437.76 ± 32.05 ms; p < 0.05). Regarding moxifloxacin, maximum values were reached, on average, 5.01 h after administration for [Formula: see text]-index (30.70 ms ± 8.32 ms versus 40.48 ms ± 7.61 ms; p < 0.05), and 4.37 h for QTc (404.29 ms ± 29.05 ms versus 426.77 ± 36.67 ms; p < 0.05). They were statistically different from baseline values. With both drugs, the maximal percent variation after administration was higher for [Formula: see text]-index than QTc (moxifloxacin: 34.56% ± 24.60% versus 5.56% ± 2.98% ; sotalol: 114.77% ± 33.15% versus 12.13% ± 2.85% ; p < 0.05).The study suggests that the standard deviation of the ventricular repolarization times, as quantified by the [Formula: see text]-index, might be an effective measure of spatial heterogeneity.

Falling in elderly is a worldwide major problem because it can lead to severe injuries, and even sudden death. Fall risk prediction would provide rapid intervention, as well as reducing the over burden of healthcare systems. Such prediction is currently performed by means of clinical scales. Among them, the Tinetti Scale is one of the better established and mostly used in clinical practice. In this work, we proposed an automatic method to assess the Tinetti scores using a wearable accelerometer. The balance and gait characteristics of 13 elderly subjects have been scored by an expert clinician while performing 8 different motor tasks according to the Tinetti Scale protocol. Two statistical analysis were selected. First, a linear regression study was performed between the Tinetti scores and 8 features (one feature for each task). Second, the generalization quality of the regression model was assessed using a Leave-One SubjectOut approach. The multiple linear regression provided a high correlation between the Tinetti scores and the features proposed (adj. R(2) = 0.948; p = 0.003). Moreover, six of the eight features added statistically significantly to the prediction of the scores (p <; 0.05). When testing the generalization capability of the model, a moderate linear correlation was obtained (R(2) = 0.67; p <; 0.05). The results suggested that the automatic method might be a promising tool to assess the falling risk of older individuals.

Background: Fetal heart rate (FHR) variability is an indirect index of fetal autonomic nervous system (ANS) integrity. FHR variability analysis in labor fails to detect early hypoxia and acidemia. Phase-rectified signal averaging (PRSA) is a new method of complex biological signals analysis that is more resistant to non-stationarities, signal loss and artifacts. It quantifies the average cardiac acceleration and deceleration (AC/DC) capacity., Objective: The aims of the study were: (1) to investigate AC/DC in ovine fetuses exposed to acute hypoxic-acidemic insult; (2) to explore the relation between AC/DC and acid-base balance; and (3) to evaluate the influence of FHR decelerations and specific PRSA parameters on AC/DC computation., Methods: Repetitive umbilical cord occlusions (UCOs) were applied in 9 pregnant near-term sheep to obtain three phases of MILD, MODERATE, and SEVERE hypoxic-acidemic insult. Acid-base balance was sampled and fetal ECGs continuously recorded. AC/DC were calculated: (1) for a spectrum of T values (T = 1÷50 beats; the parameter limits the range of oscillations detected by PRSA); (2) on entire series of fetal RR intervals or on "stable" series that excluded FHR decelerations caused by UCOs., Results: AC and DC progressively increased with UCOs phases (MILD vs. MODERATE and MODERATE vs. SEVERE, p<0.05 for DC [Formula: see text] = 2-5, and AC [Formula: see text] = 1-3). The time evolution of AC/DC correlated to acid-base balance (0.4<[Formula: see text]<0.9, p<0.05) with the highest [Formula: see text] for [Formula: see text]. PRSA was not independent from FHR decelerations caused by UCOs., Conclusions: This is the first in-vivo evaluation of PRSA on FHR analysis. In the presence of acute hypoxic-acidemia we found increasing values of AC/DC suggesting an activation of ANS. This correlation was strongest on time scale dominated by parasympathetic modulations. We identified the best performing [Formula: see text] parameters ([Formula: see text]), and found that AC/DC computation is not independent from FHR decelerations. These findings establish the basis for future clinical studies.

Lempel-Ziv Complexity (LZC) has been demonstrated to be a powerful complexity measure in several biomedical applications. During sleep, it is still not clear how many samples are required to ensure robustness of its estimate when computed on beat-to-beat interval series (RR). The aims of this study were: i) evaluation of the number of necessary samples in different sleep stages for a reliable estimation of LZC; ii) evaluation of the LZC when considering inter-subject variability; and iii) comparison between LZC and Sample Entropy (SampEn). Both synthetic and real data were employed. In particular, synthetic RR signals were generated by means of AR models fitted on real data. The minimum number of samples required by LZC for having no changes in its average value, for both NREM and REM sleep periods, was 10(4) (p<;0.01) when using a binary quantization. However, LZC can be computed with N >1000 when a tolerance of 5% is considered satisfying. The influence of the inter-subject variability on the LZC was first assessed on model generated data confirming what found (>10(4); p<;0.01) for both NREM and REM stage. However, on real data, without differentiate between sleep stages, the minimum number of samples required was 1.8×10(4). The linear correlation between LZC and SampEn was computed on a synthetic dataset. We obtained a correlation higher than 0.75 (p<;0.01) when considering sleep stages separately, and higher than 0.90 (p<;0.01) when stages were not differentiated. Summarizing, we suggest to use LZC with the binary quantization and at least 1000 samples when a variation smaller than 5% is considered satisfying, or at least 10(4) for maximal accuracy. The use of more than 2 levels of quantization is not recommended.

Objectives: We investigated if cardiac spatial repolarization heterogeneity might be associated with an increased risk of death in patients with chronic Chagas disease., Methods: Repolarization heterogeneity was assessed using the V-index, a recently introduced metric founded on a biophysical model of the ECG. This metric provides an estimate of the standard deviation of the repolarization times across the heart. We analyzed 113 patients (aged 21- 67 years) enrolled between 1998 and 1999 who had a known serological status showing positive reactions to Trypanosoma cruzi. Fourteen subjects died during a 10-year follow-up period., Results: The V-index was significantly lower in survivor (S) than in non-survivor (NS) subjects (S: 31.2 ± 13.3 ms vs NS: 41.2 ± 18.6 ms, single-tail t-test: p = 0.009, single-tail Wilcoxon rank sum test: p = 0.029). A V-index larger than 36.3 ms was related to a significantly higher risk of death in a univariate Cox proportional-hazards analysis (hazard ratio, HR = 5.34, p = 0.0046). In addition, V-index > 36.3 ms retained its prognostic value in a multivariate Cox proportional-hazards analysis after adjustment for other three clinical variables (left ventricular ejection factor < 0.50, QRS duration > 133 ms, ventricular tachycardia during stress testing or 24 hours Holter) and for T-wave amplitude variability > 30 μV, even using shrinkage, a statistical procedure that protects against over-fitting due to small sample size., Conclusions: The study showed that an increased dispersion of repolarization times in patients with Chagas disease, as measured by the V-index, is significantly correlated with the risk of death in a univariate survival analysis. The V-index captures prognostic information not immediately available from the analysis of other established risk factors.

During atrial fibrillation (AF), cancellation of ventricular activity from atrial electrograms (AEG) is commonly performed by template matching and subtraction (TMS): a running template, built in correspondence of QRSs, is subtracted from the AEG to uncover atrial activity (AA). However, TMS can produce poor cancellation, leaving high-power residues. In this study, we propose to modulate the templates before subtraction, in order to make the residuals as similar as possible to the nearby atrial activity, avoiding high-power ones. The coefficients used to modulate the template are estimated by maximizing, via Multi-swarm Particle Swarm Optimization, a fitness function. The modulated TMS method (mTMS) was tested on synthetic and real AEGs. Cancellation performances were assessed using: normalized mean squared error (NMSE, computed on simulated data only), reduction of ventricular activity (VDR), and percentage of segments (PP) whose power was outside the standard range of the atrial power. All testings suggested that mTMS is an improvement over TMS alone, being, on simulated data, NMSE and PP significantly decreased while VDR significantly increased. Similar results were obtained on real electrograms (median values of CS1 recordings PP: 2.44 vs. 0.38 p < 0.001; VDR: 6.71 vs. 8.15 p < 0.001)., (Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.)