In this paper, we review the information accumulated over the years regarding the phenomena of resetting and entrainment of reentrant arrhythmias. Over three decades of research and clinical applications, these phenomena have demonstrated that they stay as a main tool for an intellectual understanding of reentry and to base strategies for localization of critical areas for ablative therapies. This review will be divided into two parts. This first part deals with the bases for the concept development, the means for the detection of these phenomena, and their mechanistic implications. Resetting is described as a particular response of a given rhythm to an external perturbation, indicating interaction between them. Entrainment indicates continuous reset of the rhythm when the perturbation is repetitive. The mechanisms that explain these responses in reentrant rhythms are presented. Fusion, both at the surface electrocardiogram and at the level of intracardiac recordings, is discussed in detail, with its value and limitations as a key concept to recognize entrainment and reentry. Computer simulations are used as an aid to a better understanding. Differences between resetting and entrainment are considered, and a pacing protocol to study these phenomena described.

Objective: To examine the prevalence of novel newborn types among 165 million live births in 23 countries from 2000 to 2021. Design: Population‐based, multi‐country analysis. Setting: National data systems in 23 middle‐ and high‐income countries. Population: Liveborn infants. Methods: Country teams with high‐quality data were invited to be part of the Vulnerable Newborn Measurement Collaboration. We classified live births by six newborn types based on gestational age information (preterm <37 weeks versus term ≥37 weeks) and size for gestational age defined as small (SGA, 90th centile) for gestational age, according to INTERGROWTH‐21st standards. We considered small newborn types of any combination of preterm or SGA, and term + LGA was considered large. Time trends were analysed using 3‐year moving averages for small and large types. Main outcome measures: Prevalence of six newborn types. Results: We analysed 165 017 419 live births and the median prevalence of small types was 11.7% – highest in Malaysia (26%) and Qatar (15.7%). Overall, 18.1% of newborns were large (term + LGA) and was highest in Estonia 28.8% and Denmark 25.9%. Time trends of small and large infants were relatively stable in most countries. Conclusions: The distribution of newborn types varies across the 23 middle‐ and high‐income countries. Small newborn types were highest in west Asian countries and large types were highest in Europe. To better understand the global patterns of these novel newborn types, more information is needed, especially from low‐ and middle‐income countries.

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.

Holter systems record the electrocardiogram (ECG), which is used to identify beat families according to their origin and severity. Many systems have been proposed using signal conditioning and machine learning (ML) classification algorithms for beat family recognition. However, the design stage of these systems does not always consider the impact that tuning the intermediate blocks has on the beat family classification and the overall accuracy. We propose to use a new index based on the confusion matrices and bootstrap resampling to summarize the global performance for all family beats, so-called differential beat accuracy (DBA), which is obtained as the total number of beats correctly classified in each class minus the total number of beats incorrectly classified. We addressed the sensitivity of the different subblocks when creating a simple beat family classifier consisting of signal preprocessing blocks and a simple k-Nearest Neighbors classifier. The MIT-BIH Arrhythmia database was used for this purpose, following existing literature on the field.We benchmarked two implementations, one for biclass classification (supraventricular vs. non-supraventricular origin) and another for multiclass beat labeling. The usual preprocessing stages were scrutinized with the DBA to evaluate their impact on the quality of the complete ML system, such as signal detrending and filtering, beat balancing, or inter-beat distance. With the support of the DBA, our methodology was able to detect significant differences in terms of some of the options in the algorithm design. For instance, balancing the number of beats in each class for training significantly improved the classification accuracy of the minority classes at 3.22% for the multiclass dataset but not for the biclass dataset. Also, accuracy improved significantly by about 6% for the biclass regrouping without data normalization, whereas overall accuracy improved significantly by about 7% for the multiclass regrouping with data normalization. In addition, the analysis of the statistical dispersion of confusion matrices showed that this database should be considered with caution when training ML-based family classifiers. We can conclude that the proposed DBA can provide us with statistically principled criteria for designing ML-based classifiers and reducing their bias in strongly unbalanced beat family datasets.

The inverse problem in electrophysiology consists of the accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at short distances and from outside the heart. This estimation can provide an image with relevant knowledge on arrhythmia mechanisms for the clinical practice. Methods based on truncated singular value decomposition (TSVD) and regularized least squares require a matrix inversion, which limits their resolution due to the unavoidable low-pass filter effect of the Tikhonov regularization techniques. We propose to use, for the first time, a Mercer’s kernel given by the Laplacian of the distance in the quasielectrostatic field equations, hence providing a Support Vector Regression (SVR) formulation by following the principles of the Dual Signal Model (DSM) principles for creating kernel algorithms. Simulations in one- and two-dimensional models show the performance of our Laplacian distance kernel technique versus several conventional methods. Firstly, the one-dimensional model is adjusted for yielding recorded electrograms, similar to the ones that are usually observed in electrophysiological studies, and suitable strategy is designed for the free-parameter search. Secondly, simulations both in one- and two-dimensional models show larger noise sensitivity in the estimated transfer matrix than in the observation measurements, and DSM−SVR is shown to be more robust to noisy transfer matrix than TSVD. These results suggest that our proposed DSM−SVR with Laplacian distance kernel can be an efficient alternative to improve the resolution in current and emerging intracardiac imaging systems.

In recent years, a wide variety of Machine Learning (ML) algorithms, including Deep Learning (DL) methods, have been proposed for electrocardiogram (ECG) beat classification. However, accurately discerning ECG beat types faces challenges due to noise interference and inherent imbalances among different classes. Moreover, understanding mathematical models enclosed by black-box learning systems is an open issue today. Our study employed a manifold learning algorithm capable of mapping high-dimensional data into a latent space to conduct a comprehensive analysis within a neural network learning framework. This approach involved the following studies: (1) Examining the intermediate high-dimensional latent space in simple architectures by studying its projection into a visualizable latent space; (2) Exploring the influence of class imbalance on the configuration of the latent space; (3) Evaluating and analyzing the compensatory effects of employing diverse DL architectures, such as modified autoencoders and Generative Adversarial Networks (GANs), specifically in generating data augmentation through synthetic beats. The experimental results demonstrated the effectiveness of our methodology in mitigating noise and addressing inter-class imbalance, notably enhancing the diagnostic Area Under the ROC Curve (AUC) in ECG signal analysis. Implementation of GAN data augmentation techniques resulted in a 2% improvement, elevating the AUC from 0, 9332 to 0, 9520 in the biclass dataset. Similarly, the AUC values increased by 2%, from 0, 9020 to 0, 9223, for the multiclass dataset. These findings highlight the impact of appropriate data augmentation techniques on AUC improvement. Furthermore, visualizing latent spaces during beat classifier design significantly contributes to the development of solid and principled multiclass beat-discriminating systems.