Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

The Place and Value of Sodium-Glucose Cotransporter 2 Inhibitors in the Evolving Treatment Paradigm for Type 2 Diabetes Mellitus: A Narrative Review

Over recent years, the expanding evidence base for sodium-glucose cotransporter-2 inhibitor (SGLT2i) therapies has revealed benefits beyond their glucose-lowering efficacy in the treatment of Type 2 diabetes mellitus (T2DM), resulting in their recognition as cardiorenal medicines. While SGLT2is continue to be recommended among the second-line therapies for the treatment of hyperglycaemia, their true value now extends to the prevention of debilitating and costly cardiovascular and renal events for high-risk individuals, with particular benefit shown in reducing major adverse cardiac events and heart failure (HF) and slowing the progression of chronic kidney disease. However, SGLT2i usage is still suboptimal among groups considered to be at greatest risk of cardiorenal complications. The ongoing coronavirus disease 2019 (COVID-19) pandemic has intensified financial pressures on healthcare systems, which may hamper further investment in newer effective medicines. Emerging evidence indicates that glycaemic control should be prioritised for people with T2DM in the era of COVID-19 and practical advice on the use of T2DM medications during periods of acute illness remains important, particularly for healthcare professionals working in primary care who face multiple competing priorities. This article provides the latest update from the Improving Diabetes Steering Committee, including perspectives on the value of SGLT2is as cost-effective therapies within the T2DM treatment paradigm, with particular focus on the latest published evidence relating to the prevention or slowing of cardiorenal complications. The implications for ongoing and future approaches to diabetes care are considered in the light of the continuing coronavirus pandemic, and relevant aspects of international treatment guidelines are highlighted with practical advice on the appropriate use of SGLT2is in commonly occurring T2DM clinical scenarios. The ‘SGLT2i Prescribing Tool for T2DM Management’, previously published by the Steering Committee, has been updated to reflect the latest evidence and is provided in the Supplementary Materials to help support clinicians delivering T2DM care

Progression and mortality in patients with CKD attending outpatient nephrology clinics across Europe: A novel analytic approach

The incidence of renal replacement therapy (RRT) varies across countries. Yet, little is known about the epidemiology of chronic kidney disease (CKD) outcomes. Our aim was to describe progression and mortality risk in CKD patients not on RRT attending outpatient nephrology clinics across Europe. We used individual data from nine CKD cohorts participating in the European CKD Burden Consortium. A joint model was used to estimate mean eGFR change and mortality risk simultaneously, thereby accounting for mortality risk when estimating eGFR decline and vice versa, while also correcting for the measurement error in eGFR. Results were adjusted for important risk factors (baseline eGFR, age, sex, albuminuria, primary renal disease, diabetes, hypertension, obesity and smoking). 27,771 patients from five countries were included. The adjusted mean annual eGFR decline varied from 0.77 (95%CI 0.45,1.08) ml/min/1.73m2 in the Belgium cohort to 2.43 (95%CI 2.11,2.75) ml/min/1.73m2 in the Spanish cohort. As compared to the Italian PIRP cohort, the adjusted mortality hazard ratio varied from 0.22 (95%CI 0.11,0.43) in the London LACKABO cohort to 1.30 (95%CI 1.13,1.49) in the English CRISIS cohort. Outcomes in CKD patients attending outpatient nephrology clinics varied markedly across European regions. Although eGFR decline showed minor variation, the most variation was observed in CKD mortality. Our results suggest that different healthcare organization systems are potentially associated with differences in outcome of CKD patients within Europe. These results can be used by policy makers to plan resources on a regional, national and European level

Case-Based Statistical Learning: A Non-Parametric Implementation with a Conditional-Error Rate SVM

© 2013 IEEE. Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis and so on. In the theory of semi-supervised learning, we have a training set and an unlabeled data, that are employed to fit a prediction model or learner, with the help of an iterative algorithm, such as the expectation-maximization algorithm. In this paper, a novel non-parametric approach of the so-called case-based statistical learning is proposed in a low-dimensional classification problem. This supervised feature selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. To have a more accurate prediction by considering the unlabeled points, the distribution of unlabeled examples must be relevant for the classification problem. The estimation of the error rates from a well-trained support vector machines allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test

Assessing the correlation between location and size of catastrophic breakdown events in high-K MIM capacitors

The connection between the spatial location of catastrophic breakdown spots occurring in metal-insulator-metal capacitors with a high-permittivity dielectric film (HfO 2 ) and their respective sizes is investigated. Large area structures (10 4 -10 5 μm 2 ) are used for this correlation assessment since, for statistical considerations, a large number of spots in the same device is imperatively required. The application of ramped or constant voltage stress across the capacitor generates defects inside the dielectric that result in the formation of multiple failure sites. High power dissipation takes place locally, leaving a permanent mark on the top electrode of the device. The set of marks constitutes a point pattern with attributes that can be analyzed from a statistical viewpoint. The correlation between the spot locations and their sizes is assessed through the mark correlation function and the method of reverse conditional moments. The study reveals that for severely damaged devices, there exists a link between the spot location and size that leads to a short range departure from a complete spatial randomness (CSR) process. It is shown that the affected region around each failure site is actually larger than the visible area of the spot. A structural modification of the dielectric layer in the vicinity of the spot caused by the huge thermal effects occurring just before the microexplosion might be the reason behind this extension of the damage

Active collaboration with primary care providers increases specialist referral in chronic renal disease

BACKGROUND: Late referral to specialist nephrological care is associated with increased morbidity, mortality, and cost. Consequently, nephrologists' associations recommend early referral. The recommendations' effectiveness remains questionable: 22–51% of referrals need renal replacement therapy (RRT) within 3–4 months. This may be due to these recommendations addressing the specialist, rather than the primary care providers (PCP). The potential of specialist intervention aiming at slowing progression of chronic renal failure was introduced individually to some 250 local PCPs, and referral strategies were discussed. To overcome the PCPs' most often expressed fears, every referred patient was asked to report back to his PCP immediately after the initial specialist examination, and new medications were prescribed directly, and thus allotted to the nephrologist's budget. METHODS: In retrospective analysis, the stage of renal disease in patients referred within three months before the introductory round (group A, n = 18), was compared to referrals two years later (group B, n = 50). RESULTS: Relative number of patients remained stable (28%) for mild/ moderate chronic kidney disease (MMCKD), while there was a noticeable shift from patients referred severe chronic kidney disease (SCKD) (group A: 44%, group B: 20%) to patients referred in moderate chronic kidney disease (MCKD) (group A: 28%, group B: 52%). CONCLUSION: Individually addressing PCPs' ignorance and concerns noticeably decreased late referral. This stresses the importance of enhancing the PCPs' problem awareness and knowledge of available resources in order to ensure timely specialist referral

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging