We conducted a systematic review of the literature for assessing the value of home monitoring for heart failure (HF) patients. The abstracts of 383 articles were read. We excluded those in which either no home monitoring was done or only the technical aspects of the telemedicine application were described. Forty-two studies met the selection criteria. We classified the results into feasibility (technical and institutional) and impact (on the clinical process, on patient health, on accessibility and acceptability of the health system, and on the economy). Evaluating the articles showed that home monitoring in HF patients is viable, given that: (1) it appears to be technically effective for following the patient remotely; (2) it appears to be easy to use, and it is widely accepted by patients and health professionals; and (3) it appears to be economically viable. Furthermore, home monitoring of HF patients has been shown to have a positive impact on: (1) the clinical process, supported by a significant improvement of patient follow-up by adjustment of treatment, diet or behaviour, as well as hospital readmissions and emergency visits reduction; (2) the patient's health, supported by a relevant improvement in quality of life, a reduction of days in hospital, and a decrease in mortality; and (3) costs resulting from the use of health resources.

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This paper deals with the application of the Support Vector Method (SVM) methodology to the Auto Regressive and Moving Average (ARMA) linear-system identification problem. The SVM-ARMA algorithm for a single-input single-output transfer function is formulated. The relationship between the SVM coefficients and the residuals, together with the embedded estimation of the autocorrelation function, are presented. Also, the effect of the numerical regularization is used to highlight the robust cost character of this approach. A clinical example is presented for qualitative comparison with the classical Least Squares (LS) methods.

Background-Ejection intraventricular pressure gradients are caused by the systolic force developed by the left ventricle (LV). By postprocessing color Doppler M-mode (CDMM) images, we can measure noninvasively the ejection intraventricular pressure difference (EIVPD) between the LV apex and the outflow tract. This study was designed to assess the value of Doppler-derived EIVPDs as noninvasive indices of systolic chamber function. Methods and Results-CDMM images and pressure-volume (conductance) signals were simultaneously acquired in 9 minipigs undergoing pharmacological interventions and acute ischemia. Inertial, convective, and total EIVPD curves were calculated from CDMM recordings. Peak EIVPD closely correlated with indices of systolic function based on the pressure-volume relationship: peak elastance (within-animal Rϭ0.98; between-animals Rϭ0.99), preload recruitable stroke work , and peak of the first derivative of pressure corrected for end-diastolic volume (within-animal Rϭ0.88; between-animals Rϭ0.91). The correlation of peak inertial EIVPD with these indices was also high (all RϾ0.75). Load dependence of EIVPDs was studied in another 5 animals in which consecutive beats obtained during load manipulation were analyzed. During caval occlusion (40% EDV reduction), dP/dt max , ejection fraction, and stroke volume significantly changed, whereas peak EIVPD remained constant. Aortic occlusion (40% peak LV pressure increase) significantly modified dP/dt max , ejection fraction, and stroke volume; a nearly significant trend toward decreasing peak EIVPD was observed (Pϭ0.06), whereas inertial EIVPD was unchanged (Pϭ0.6). EIVPD beat-to-beat and interobserver variabilities were 2Ϯ12% and 5Ϯ11%, respectively. Conclusions-Doppler-derived

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This paper provides an overview of the support vector machine (SVM) methodology and its applicability to real‐world engineering problems. Specifically, the aim of this study is to review the current state of the SVM technique, and to show some of its latest successful results in real‐world problems present in different engineering fields. The paper starts by reviewing the main basic concepts of SVMs and kernel methods. Kernel theory, SVMs, support vector regression (SVR), and SVM in signal processing and hybridization of SVMs with meta‐heuristics are fully described in the first part of this paper. The adoption of SVMs in engineering is nowadays a fact. As we illustrate in this paper, SVMs can handle high‐dimensional, heterogeneous and scarcely labeled datasets very efficiently, and it can be also successfully tailored to particular applications. The second part of this review is devoted to different case studies in engineering problems, where the application of the SVM methodology has led to excellent results. First, we discuss the application of SVR algorithms in two renewable energy problems: the wind speed prediction from measurements in neighbor stations and the wind speed reconstruction using synoptic‐pressure data. The application of SVMs in noninvasive cardiac indices estimation is described next, and results obtained there are presented. The application of SVMs in problems of functional magnetic resonance imaging (fMRI) data processing is further discussed in the paper: brain decoding and mental disorder characterization. The following application deals with antenna array processing, namely SVMs for spatial nonlinear beamforming, and the SVM application in a problem of arrival angle detection. Finally, the application of SVMs to remote sensing image classification and target detection problems closes this review. WIREs Data Mining Knowl Discov 2014, 4:234–267. doi: 10.1002/widm.1125 This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning

For the first time, ejection IVPGs can be accurately visualized and measured by Doppler-echocardiography. Important aspects of the dynamic interaction among myocardial performance, load mechanics, and ejection dynamics can be assessed in the clinical setting using this method.

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.