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Introduction and objectives: The choice of renal replacement therapy (RRT) is an important decision that determines the quality of life and survival. Most patients change from one RRT modality to another to adapt RRT to clinical and psychosocial needs. This has been called «integrated model of RRT» that implies new questions about the best sequence of techniques. Material and methods: The study describes the impact of transitions between RRT modalities on survival using the Madrid Registry of Renal Patients (2008–2018). This study used the proportional hazards models and competitive risk models to perform an intention-to-treat (ITT), according to their 1st RRT modality and as-treated (AT) analysis, that consider also their 1st transition. Results: A total of 8971 patients started RRT during this period in Madrid (6.6 Million population): 7207 (80.3%) on hemodialysis (HD), 1401 (15.6%) on peritoneal dialysis (PD) and 363 (4.2%) received a pre-emptive kidney transplantation (KT). Incident HD-patients were older (HD group 65.3 years (SD 15.3) vs PD group 58.1 years (SD 14.8) vs KTX group 52 years (SD 17.2); p

Resumen: Introducción y objetivos: La elección del tratamiento sustitutivo renal (TSR) es una decisión importante que determina la calidad de vida y la supervivencia. La mayoría de los pacientes cambiará de una modalidad de TSR a otra para adaptarla a sus necesidades dentro de lo que se conoce como modelo de TSR integrado. En estas circunstancias surgen nuevas preguntas sobre la mejor secuencia de técnicas o las consecuencias de las transiciones. Material y métodos: Describimos las transiciones entre técnicas de TSR y su impacto en la supervivencia a partir del Registro Madrileño de Enfermos Renales (REMER), durante un periodo de 11 años. Se utilizaron los modelos de riesgos proporcionales y de riesgos competitivos para realizar un análisis por intención de tratar (ITT) según su 1.er tratamiento y como tratado (AT) considerando la 1.ª transición. Resultados: Un total de 8.971 pacientes iniciaron su primer TSR durante este periodo en Madrid (6,6 millones habitantes): 7.207 (80,3%) en hemodiálisis (HD), 1.401 (15,6%) en diálisis peritoneal (DP) y 363 (4,1%) recibieron un trasplante renal anticipado (TXR). En el análisis ITT, los pacientes incidentes en HD eran mayores (HD 65,3 años (DE 15,3) vs. DP 58,1 años [DE 14,8] vs. TXR 52,0 años (DE 17,2); p

This article provides a review of the Citizen Broadband Radio Service (CBRS) and its characteristics. It also describes the Spectrum Access System, which allows the bands not used by primary users to be intelligently occupied by secondary users to optimize this resource. As a rule, CBRS operates between 3550 MHz and 3700 MHz to allow users’ connectivity from different operators in high-traffic locations. Using Neutral Hosts minimizes the installation and operation costs of the Advanced Mobile Service’s operators. CBRS also functions as a private LTE network with several advantages over other wireless network technologies. This innovative proposal marks a milestone in shifting from fixed allocation of the radio spectrum to dynamic controlled access, enabling connectivity to a wide range of wireless devices.

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.

Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify Culicoides species. A database containing wing images of C. obsoletus, C. pusillus, C. foxi, and C. insignis species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between C. obsoletus and C. pusillus species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between C. foxi and C. insignis species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.

Abstract Background 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. Methods 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. Results 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. Conclusion 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.

COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies in hospitality industry by generating a great amount of valuable information about clients, whereas Big Data tools are providing with unprecedented facilities to conduct massive analysis and to focus the client-to-business relationship. However, few instruments have been proposed to handle categorical features, which are the most usual in CRMs, aiming to adapt the statistical robustness with the best interpretability for the managers. Therefore, our aim was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system. An analysis method was created involving three elements: First, Multiple Correspondence Analysis provides us with a statistical description of the interactions among categories and features. Second, bootstrap resampling techniques give us information about the statistical variability of the feature maps. Third, kernel methods provide easy-to-visualize domain descriptions based on confidence areas in the maps. The proposed methodology can provide an operative and statistically principled way to scrutinize the CRM profiles in hospitality.

Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH.

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IVTM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF.

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial–temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial–temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial–temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial–temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.

Deep Learning networks are revolutionizing both the academic and the industrial scenarios of information and communication technologies [...]

Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500−698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting.

During the last decades there has been a rapidly growing elderly population and the number of patients with chronic heart-related diseases has exploded. Many of them (such as those with congestive heart failure or some types of arrhythmias) require close medical supervision, thus imposing a big burden on healthcare costs in most western economies. Specifically, continuous or frequent Arterial Blood Pressure (ABP) and electrocardiogram (ECG) monitoring are important tools in the follow-up of many of these patients. In this work, we present a novel remote non-ambulatory and clinically validated heart self-monitoring system, which allows ABP and ECG monitoring to effectively identify clinically relevant arrhythmias. The system integrates digital transmission of the ECG and tensiometer measurements, within a patient-comfortable support, easy to recharge and with a multi-function software, all of them aiming to adapt for elderly people. The main novelty is that both physiological variables (ABP and ECG) are simultaneously measured in an ambulatory environment, which to our best knowledge is not readily available in the clinical market. Different processing techniques were implemented to analyze the heart rhythm, including pause detection, rhythm alterations and atrial fibrillation, hence allowing early detection of these diseases. Our results achieved clinical quality both for in-lab hardware testing and for ambulatory scenario validations. The proposed active assisted living (AAL) Sensor-based system is an end-to-end multidisciplinary system, fully connected to a platform and tested by the clinical team from beginning to end.

Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

The identification of patients with increased risk of Sudden Cardiac Death (SCD) has been widely studied during recent decades, and several quantitative measurements have been proposed from the analysis of the electrocardiogram (ECG) stored in 1-day Holter recordings. Indices based on nonlinear dynamics of Heart Rate Variability (HRV) have shown to convey predictive information in terms of factors related with the cardiac regulation by the autonomous nervous system, and among them, multiscale methods aim to provide more complete descriptions than single-scale based measures. However, there is limited knowledge on the suitability of nonlinear measurements to characterize the cardiac dynamics in current long-term monitoring scenarios of several days. Here, we scrutinized the long-term robustness properties of three nonlinear methods for HRV characterization, namely, the Multiscale Entropy (MSE), the Multiscale Time Irreversibility (MTI), and the Multifractal Spectrum (MFS). These indices were selected because all of them have been theoretically designed to take into account the multiple time scales inherent in healthy and pathological cardiac dynamics, and they have been analyzed so far when monitoring up to 24 h of ECG signals, corresponding to about 20 time scales. We analyzed them in 7-day Holter recordings from two data sets, namely, patients with Atrial Fibrillation and with Congestive Heart Failure, by reaching up to 100 time scales. In addition, a new comparison procedure is proposed to statistically compare the poblational multiscale representations in different patient or processing conditions, in terms of the non-parametric estimation of confidence intervals for the averaged median differences. Our results show that variance reduction is actually obtained in the multiscale estimators. The MSE (MTI) exhibited the lowest (largest) bias and variance at large scales, whereas all the methods exhibited a consistent description of the large-scale processes in terms of multiscale index robustness. In all the methods, the used algorithms could turn to give some inconsistency in the multiscale profile, which was checked not to be due to the presence of artifacts, but rather with unclear origin. The reduction in standard error for several-day recordings compared to one-day recordings was more evident in MSE, whereas bias was more patently present in MFS. Our results pave the way of these techniques towards their use, with improved algorithmic implementations and nonparametric statistical tests, in long-term cardiac Holter monitoring scenarios.

A public safety answering point (PSAP) receives thousands of security alerts and attends a similar number of emergencies every day, and all the information related to those events is saved to be post-processed and scrutinized. Visualization and interpretation of emergency data can provide fundamental feedback to the first-response institutions, to managers planning resource distributions, and to all the instances participating in the emergency-response cycle. This paper develops the application of multiple correspondence analysis (MCA) of emergency responses in a PSAP, with the objective of finding informative relationships among the different categories of registered and attended events. We propose a simple yet statistically meaningful method to scrutinize the variety of events and recorded information in conventional PSAPs. For this purpose, MCA is made on the categorical features of the available report forms, and a statistical description is achieved from it by combining bootstrap resampling and Parzen windowing, in order to provide the user with the most relevant factors, their significance, and a meaningful representation of the event grouping trends in a given database. We analyzed the case of the 911-emergency database from Quito, Ecuador, which includes 1,078,846 events during 2014. Individual analysis of the first-response institutions showed that there are groups with very related categories, whereas their joint analysis showed significant relationships among several types of events. This was the case for fire brigades, military, and municipal services attending large-scale forest fires, where they work in a combined way. Independence could be established among actions in other categories, which was the case for specific police events (as drug selling and distribution) or fire brigades events (as fire threats). We also showed that a very low number of factors can be enough to accurately represent the dynamics of frequent events.

Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.

In recent years, a number of proposals for electrocardiogram (ECG) monitoring based on mobile systems have been delivered. We propose here an STM32F-microcontroller-based ECG mobile system providing both long-term (several weeks) Holter monitoring and 12-lead ECG recording, according to the clinical standard requirements for these kinds of recordings, which in addition can yield further digital compression at stages close to the acquisition. The system can be especially useful in rural areas of developing countries, where the lack of specialized medical personnel justifies the introduction of telecardiology services, and the limitations of coverage and bandwidth of cellular networks require the use of efficient signal compression systems. The prototype was implemented using a small architecture, with a 16-bits-per-sample resolution. We also used a low-noise instrumentation amplifier TI ADS1198, which has a multiplexer and an analog-to-digital converter (16 bits and 8 channels) connected to the STM32F processor, the architecture of which incorporates a digital signal processing unit and a floating-point unit. On the one hand, the system portability allows the user to take the prototype in her/his pocket and to perform an ECG examination, either in 12-lead controlled conditions or in Holter monitoring, according to the required clinical scenario. An app in the smartphone is responsible for giving the users a friendly interface to set up the system. On the other hand, electronic health recording of the patients are registered in a web application, which in turn allows them to connect to the Internet from their cellphones, and the ECG signals are then sent though a web server for subsequent and ubiquitous analysis by doctors at any convenient terminal device. In order to determine the quality of the received signals, system testing was performed in the three following scenarios: (1) The prototype was connected to the patient and the signals were subsequently stored; (2) the prototype was connected to the patient and the data were subsequently transferred to the cellphone; (3) the prototype was connected to the patient, and the data were transferred to the cellphone and to the web via the Internet. An additional benchmarking test with expert clinicians showed the clinical quality provided by the system. The proposed ECG system is the first step and paves the way toward mobile cardiac monitors in terms of compatibility with the electrocardiographic practice, including the long-term monitoring, the usability with 12 leads, and the possibility of incorporating signal compression at the early stages of the ECG acquisition.

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland–Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

In recent years, attention has been paid to wireless sensor networks (WSNs) applied to precision agriculture. However, few studies have compared the technologies of different communication standards in terms of topology and energy efficiency. This paper presents the design and implementation of the hardware and software of three WSNs with different technologies and topologies of wireless communication for tomato greenhouses in the Andean region of Ecuador, as well as the comparative study of the performance of each of them. Two companion papers describe the study of the dynamics of the energy consumption and of the monitored variables. Three WSNs were deployed, two of them with the IEEE 802.15.4 standard with star and mesh topologies (ZigBee and DigiMesh, respectively), and a third with the IEEE 802.11 standard with access point topology (WiFi). The measured variables were selected after investigation of the climatic conditions required for efficient tomato growth. The measurements for each variable could be displayed in real time using either a laboratory virtual instrument engineering workbench (LabVIEWTM) interface or an Android mobile application. The comparative study of the three networks made evident that the configuration of the DigiMesh network is the most complex for adding new nodes, due to its mesh topology. However, DigiMesh maintains the bit rate and prevents data loss by the location of the nodes as a function of crop height. It has been also shown that the WiFi network has better stability with larger precision in its measurements.

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO2 sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.

Efficient energy management is strongly dependent on determining the adequate power contracts among the ones offered by different electricity suppliers. This topic takes special relevance in healthcare buildings, where noticeable amounts of energy are required to generate an adequate health environment for patients and staff. In this paper, a convex optimization method is scrutinized to give a straightforward analysis of the optimal power levels to be contracted while minimizing the electricity bill cost in a time-of-use pricing scheme. In addition, a sensitivity analysis is carried out on the constraints in the optimization problems, which are analyzed in terms of both their empirical distribution and their bootstrap-estimated statistical distributions to create a simple-to-use tool for this purpose, the so-called mosaic-distribution. The evaluation of the proposed method was carried out with five-year consumption data on two different kinds of healthcare buildings, a large one given by Hospital Universitario de Fuenlabrada, and a primary care center, Centro de Especialidades el Arroyo, both located at Fuenlabrada (Madrid, Spain). The analysis of the resulting optimization shows that the annual savings achieved vary moderately, ranging from −0.22 % to +27.39%, depending on the analyzed year profile and the healthcare building type. The analysis introducing mosaic-distribution to represent the sensitivity score also provides operative information to evaluate the convenience of implementing energy saving measures. All this information is useful for managers to determine the appropriate power levels for next year contract renewal and to consider whether to implement demand response mechanisms in healthcare buildings.

The intracardiac electrical activation maps are commonly used as a guide in the ablation of cardiac arrhythmias. The use of catheters with force sensors has been proposed in order to know if the electrode is in contact with the tissue during the registration of intracardiac electrograms (EGM). Although threshold criteria on force signals are often used to determine the catheter contact, this may be a limited criterion due to the complexity of the heart dynamics and cardiac vorticity. The present paper is devoted to determining the criteria and force signal profiles that guarantee the contact of the electrode with the tissue. In this study, we analyzed 1391 force signals and their associated EGM recorded during 2 and 8 s, respectively, in 17 patients (82 ± 60 points per patient). We aimed to establish a contact pattern by first visually examining and classifying the signals, according to their likely-contact joint profile and following the suggestions from experts in the doubtful cases. First, we used Principal Component Analysis to scrutinize the force signal dynamics by analyzing the main eigen-directions, first globally and then grouped according to the certainty of their tissue-catheter contact. Second, we used two different linear classifiers (Fisher discriminant and support vector machines) to identify the most relevant components of the previous signal models. We obtained three main types of eigenvectors, namely, pulsatile relevant, non-pulsatile relevant, and irrelevant components. The classifiers reached a moderate to sufficient discrimination capacity (areas under the curve between 0.84 and 0.95 depending on the contact certainty and on the classifier), which allowed us to analyze the relevant properties in the force signals. We conclude that the catheter-tissue contact profiles in force recordings are complex and do not depend only on the signal intensity being above a threshold at a single time instant, but also on time pulsatility and trends. These findings pave the way towards a subsystem which can be included in current intracardiac navigation systems assisted by force contact sensors, and it can provide the clinician with an estimate of the reliability on the tissue-catheter contact in the point-by-point EGM acquisition procedure.

Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels.

A public safety answering point (PSAP) receives alerts and attends to emergencies that occur in its responsibility area. The analysis of the events related to a PSAP can give us relevant information in order to manage them and to improve the performance of the first response institutions (FRIs) associated to every PSAP. However, current emergency systems are growing dramatically in terms of information heterogeneity and the volume of attended requests. In this work, we propose a system for statistical, spatial, and temporal analysis of incidences registered in a PSAP by using simple, yet robust and compact, event representations. The selected and designed temporal analysis tools include seasonal representations and nonparametric confidence intervals (CIs), which dissociate the main seasonal components and the transients. The spatial analysis tools include a straightforward event location over Google Maps and the detection of heat zones by means of bidimensional geographic Parzen windows with automatic width control in terms of the scales and the number of events in the region of interest. Finally, statistical representations are used for jointly analyzing temporal and spatial data in terms of the “time–space slices”. We analyzed the total number of emergencies that were attended during 2014 by seven FRIs articulated in a PSAP at the Ecuadorian 911 Integrated Security Service. Characteristic weekly patterns were observed in institutions such as the police, health, and transit services, whereas annual patterns were observed in firefighter events. Spatial and spatiotemporal analysis showed some expected patterns together with nontrivial differences among different services, to be taken into account for resource management. The proposed analysis allows for a flexible analysis by combining statistical, spatial and temporal information, and it provides 911 service managers with useful and operative information.

Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning system intended to provide an estimation of the LVTES anatomical region with the use of ICD-EGM in the situation where 12-lead electrocardiogram of ventricular tachycardia are not available. Several machine learning techniques were specifically designed and benchmarked, both from classification (such as Neural Networks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accuracy rates for LVTES identification in a controlled number of anatomical regions, and the regression approach quality was studied in terms of the spatial resolution. We analyzed the ICD-EGM of 23 patients (18±10 EGM per patient) during left ventricular pacing and simultaneous recording of the spatial coordinates of the pacing electrode with a navigation system. Several feature sets extracted from ICD-EGM (consisting of times and voltages) were shown to convey more discriminative information than the raw waveform. Among classifiers, the SVM performed slightly better than NN. In accordance with previous clinical works, the average spatial resolution for the LVTES was about 3 cm, as in our system, which allows it to support the faster determination of the LVTES in ablation procedures. The proposed approach also provides with a framework suitable for driving the design of improved performance future systems.

Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise.