Joint movement patterns differ among male recreational runners with different running style.

The purpose of this study was to analyse the differences in joint kinematic patterns among runners with different spatiotemporal characteristics in the running cycle. Lower extremity kinematic data and spatiotemporal stride parameters were collected for ninety-two recreational runners during a treadmill run at a self-selected comfortable speed. A K-means clustering analysis was conducted on normalised stride cadence and Duty Factor to identify running style. Cluster 1 characterised by reduced stance times and low Duty Factor; Cluster 2, long stance times and low stride cadence; Cluster 3, high Duty Factor and stride cadence. Functional principal component analysis was used to identify patterns of variability between runners. Runners who used a combination of high cadence and Duty Factor showed differences in hip, knee and ankle sagittal kinematics compared to other runners. On the contrary, the joint kinematics was not altered when the Duty Factor was increased along with a decrease in the stride cadence. This study has demonstrated that the combination of several spatial-temporal parameters of the running cycle should be considered when analysing the movement pattern of the lower limb.

Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.

The causes of ventricular fibrillation (VF) are not yet elucidated, and it has been proposed that different mechanisms might exist. Moreover, conventional analysis methods do not seem to provide time or frequency domain features that allow for recognition of different VF patterns in electrode-recorded biopotentials. The present work aims to determine whether low-dimensional latent spaces could exhibit discriminative features for different mechanisms or conditions during VF episodes. For this purpose, manifold learning using autoencoder neural networks was analyzed based on surface ECG recordings. The recordings covered the onset of the VF episode as well as the next 6 min, and comprised an experimental database based on an animal model with five situations, including control, drug intervention (amiodarone, diltiazem, and flecainide), and autonomic nervous system blockade. The results show that latent spaces from unsupervised and supervised learning schemes yielded moderate though quite noticeable separability among the different types of VF according to their type or intervention. In particular, unsupervised schemes reached a multi-class classification accuracy of 66%, while supervised schemes improved the separability of the generated latent spaces, providing a classification accuracy of up to 74%. Thus, we conclude that manifold learning schemes can provide a valuable tool for studying different types of VF while working in low-dimensional latent spaces, as the machine-learning generated features exhibit separability among different VF types. This study confirms that latent variables are better VF descriptors than conventional time or domain features, making this technique useful in current VF research on elucidation of the underlying VF mechanisms.

Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure.

BACKGROUND/AIM: In atrial fibrillation (AF) ablation procedures, it is desirable to know whether a proper disconnection of the pulmonary veins (PVs) was achieved. We hypothesize that information about their isolation could be provided by analyzing changes in P-wave after ablation. Thus, we present a method to detect PV disconnection using P-wave signal analysis. METHODS: Conventional P-wave feature extraction was compared to an automatic feature extraction procedure based on creating low-dimensional latent spaces for cardiac signals with the Uniform Manifold Approximation and Projection (UMAP) method. A database of patients (19 controls and 16 AF individuals who underwent a PV ablation procedure) was collected. Standard 12-lead ECG was recorded, and P-waves were segmented and averaged to extract conventional features (duration, amplitude, and area) and their manifold representations provided by UMAP on a 3-dimensional latent space. A virtual patient was used to validate these results further and study the spatial distribution of the extracted characteristics over the whole torso surface. RESULTS: Both methods showed differences between P-wave before and after ablation. Conventional methods were more prone to noise, P-wave delineation errors, and inter-patient variability. P-wave differences were observed in the standard leads recordings. However, higher differences appeared in the torso region over the precordial leads. Recordings near the left scapula also yielded noticeable differences. CONCLUSIONS: P-wave analysis based on UMAP parameters detects PV disconnection after ablation in AF patients and is more robust than heuristic parameterization. Moreover, additional leads different from the standard 12-lead ECG should be used to detect PV isolation and possible future reconnections better.

Transitions in an integrated model of renal replacement therapy in a regional health system.

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 RRT¼ 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 < 0.001) and had more comorbidities. They presented higher mortality (HD group 40.9% vs PD group 22.8% vs KTX group 8.3%, p < 0.001) and less access to a transplant (HD group 30.4% vs PD group 51.6%; p < 0.001). Transitions between dialysis techniques define different groups of patients with different clinical outcomes. Those who change from HD to PD do it earlier (HD â†’ PD: 0.7 years (SD 1.1) vs PD â†’ HD: 1.5 years (SD 1.4) p < 0.001), are younger (HD â†’ PD: 53.5 years (SD 16.7) vs PD â†’ HD: 61.6 years (SD 14.6); p < 0.001), presented less mortality (HD â†’ PD: 24.5% vs PD â†’ HD: 32.0%; p < 0.001) and higher access to a transplant (HD â†’ PD: 49.4% vs PD â†’ HD: 31.7%; p < 0.001). Survival analysis by competitive risks is essential for integrated RRT models, especially in groups such as PD patients, where 51.6% of the patients were considered as lost follow-up (received a KTX after during the first 2.5 years on PD). In this analysis, survival of patients who change from one technique to another, is more similar to the destination modality than the origin one. CONCLUSION: Our data suggest that transitions between RRT-techniques describes different patients, who associate different risks, and could be analyzed in an integrated manner to define improvement actions. This approach should be incorporated into the analysis and reports of renal registries.

Generalization and Regularization for Inverse Cardiac Estimators.

Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.

Transitions in an integrated model of renal replacement therapy in a regional health system. / Análisis de las transiciones en el modelo integrado de tratamiento sustitutivo renal en un sistema regional de salud.

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 RRT¼ 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 (KTX). 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<0.001) and had more comorbidities. They presented higher mortality (HD group 40.9% vs PD group 22.8% vs 8.3% KTX group, p<0.001) and less access to a transplant (HD group 30.4% vs DP group 51.6%; p<0.001). Transitions between dialysis techniques define different groups of patients with different clinical outcomes. Those who change from HD to PD do it earlier (HD→PD: 0.7 years (SD 1.1) vs PD→HD: 1.5 years (SD 1.4) p<0.001), are younger (HD→PD: 53.5 years (SD 16.7) vs PD→HD: 61.6 years (SD 14.6); p<0.001), presented less mortality (HD→PD: 24.5% vs PD→HD: 32.0%; p<0.001) and higher access to a transplant (HD→PD: 49.4% vs PD→HD: 31.7%; p<0.001). Survival analysis by competitive risks is essential for integrated RRT models, especially in groups such as PD patients, where 51.6% of the patients were considered as lost follow-up (received a KTX after during the first 2.5 years on PD). In this analysis, survival of patients who change from one technique to another, is more similar to the destination modality than the origin one. CONCLUSION: Our data suggest that transitions between RRT-techniques describes different patients, who associate different risks, and could be analyzed in an integrated manner to define improvement actions. This approach should be incorporated into the analysis and reports of renal registries.

Automatic ladybird beetle detection using deep-learning models.

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.

Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value.

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.

Comparison of Omegawave Device and an Ambulatory ECG for RR Interval Measurement at rest.

The aim of this study was to validate the measurements of the beat intervals taken at rest by the Omegawave® device by comparing them to an ambulatory electrocardiogram system. For this purpose, the electrocardiogram was digitally processed, time-aligned, and scrutinized for its suitable use as gold-standard. Rest measurements were made for 10 minutes on 5 different days to 10 men and 3 women (24.8±5.05 years; 71.82±11.02 kg; 174.35±9.13 cm). RR intervals were simultaneously recorded using the Omegawave device and a Holter electrocardiogram. The processing of Holter electrocardiogram signals included the detrending of baseline noise and a high-pass filtering for emphasizing the QRS complexes and attenuating the T waves. After obtaining the RR intervals from the electrocardiogram, those from the Omegawave device were automatically aligned to them with cross-correlation digital processing techniques and compared to check whether both measurements could be considered superimposable. A Bland-Altman analysis was applied to the 5 measurements made for all subjects. The Omegawave device exhibited very strong agreement with a quality-controlled Holter electrocardiogram. Deviations not exceeding 25 ms could be expected in 95% of the cases, which is within manageable ranges both for clinical practice and for sports.

An approach to automatic classification of Culicoides species by learning the wing morphology.

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.

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation.

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.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials.

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.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans.

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.

Enabling Heart Self-Monitoring for All and for AAL-Portable Device within a Complete Telemedicine System.

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.

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management.

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.

On the Robustness of Multiscale Indices for Long-Term Monitoring in Cardiac Signals.

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.

Lack of improvement in autonomic cardiac tone after sacubitril/valsartan at lower than target doses.

Autonomic regulation plays a role in the progression of heart failure with reduced ejection fraction (HrEF).Twenty-one HFrEF patients, 60.8 ±â€¯13.1 years, receiving angiotensin inhibition, were replaced by angiotensin receptor-neprilysin inhibitor (ARNI). A 24-hour Holter recording was performed before and after 3 months of the maximum tolerated dose of ARNi. We evaluated changes in autonomic tone using heart rate variability (SDNN, rMSSD, pNN50, LF, HF, LF/HF, α1, α2), and heart rate turbulence (TO and TS). ARNI was up-titrated to a maximum daily dose of 190 ±â€¯102 mg, 47.5% of the target dose. ARNI therapy was not associated with any improvement in any of the parameters related with heart rate variability or heart rate turbulence (p > 0.05 for all). ARNI use at lower than target doses did not improve autonomic cardiac tone as evaluated by 24-hour Holter monitoring.

A Flexible 12-Lead/Holter Device with Compression Capabilities for Low-Bandwidth Mobile-ECG Telemedicine Applications.

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.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (I): Wireless Sensor Network Benchmarking.

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.