Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces

High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe highseverity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks

Natural History of MYH7-Related Dilated Cardiomyopathy

BACKGROUND Variants in myosin heavy chain 7 (MYH7) are responsible for disease in 1% to 5% of patients with dilated cardiomyopathy (DCM); however, the clinical characteristics and natural history of MYH7-related DCM are poorly described. OBJECTIVES We sought to determine the phenotype and prognosis of MYH7-related DCM. We also evaluated the influence of variant location on phenotypic expression. METHODS We studied clinical data from 147 individuals with DCM-causing MYH7 variants (47.6% female; 35.6 +/- 19.2 years) recruited from 29 international centers. RESULTS At initial evaluation, 106 (72.1%) patients had DCM (left ventricular ejection fraction: 34.5% +/- 11.7%). Median follow-up was 4.5 years (IQR: 1.7-8.0 years), and 23.7% of carriers who were initially phenotype-negative developed DCM. Phenotypic expression by 40 and 60 years was 46% and 88%, respectively, with 18 patients (16%) first diagnosed at <18 years of age. Thirty-six percent of patients with DCM met imaging criteria for LV noncompaction. During follow-up, 28% showed left ventricular reverse remodeling. Incidence of adverse cardiac events among patients with DCM at 5 years was 11.6%, with 5 (4.6%) deaths caused by end-stage heart failure (ESHF) and 5 patients (4.6%) requiring heart transplantation. The major ventricular arrhythmia rate was low (1.0% and 2.1% at 5 years in patients with DCM and in those with LVEF of <= 35%, respectively). ESHF and major ventricular arrhythmia were significantly lower compared with LMNA-related DCM and similar to DCM caused by TTN truncating variants. CONCLUSIONS MYH7-related DCM is characterized by early age of onset, high phenotypic expression, low left ventricular reverse remodeling, and frequent progression to ESHF. Heart failure complications predominate over ventricular arrhythmias, which are rare. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation

A Primary Prevention Clinical Risk Score Model for Patients With Brugada Syndrome (BRUGADA-RISK).

OBJECTIVES: The goal of this study was to develop a risk score model for patients with Brugada syndrome (BrS). BACKGROUND: Risk stratification in BrS is a significant challenge due to the low event rates and conflicting evidence. METHODS: A multicenter international cohort of patients with BrS and no previous cardiac arrest was used to evaluate the role of 16 proposed clinical or electrocardiogram (ECG) markers in predicting ventricular arrhythmias (VAs)/sudden cardiac death (SCD) during follow-up. Predictive markers were incorporated into a risk score model, and this model was validated by using out-of-sample cross-validation. RESULTS: A total of 1,110 patients with BrS from 16 centers in 8 countries were included (mean age 51.8 ± 13.6 years; 71.8% male). Median follow-up was 5.33 years; 114 patients had VA/SCD (10.3%) with an annual event rate of 1.5%. Of the 16 proposed risk factors, probable arrhythmia-related syncope (hazard ratio [HR]: 3.71; p < 0.001), spontaneous type 1 ECG (HR: 3.80; p < 0.001), early repolarization (HR: 3.42; p < 0.001), and a type 1 Brugada ECG pattern in peripheral leads (HR: 2.33; p < 0.001) were associated with a higher risk of VA/SCD. A risk score model incorporating these factors revealed a sensitivity of 71.2% (95% confidence interval: 61.5% to 84.6%) and a specificity of 80.2% (95% confidence interval: 75.7% to 82.3%) in predicting VA/SCD at 5 years. Calibration plots showed a mean prediction error of 1.2%. The model was effectively validated by using out-of-sample cross-validation according to country. CONCLUSIONS: This multicenter study identified 4 risk factors for VA/SCD in a primary prevention BrS population. A risk score model was generated to quantify risk of VA/SCD in BrS and inform implantable cardioverter-defibrillator prescription

Evolución de los hallazgos ecocardiográficos de la miocardiopatía hipertrófica, relación con factores de riesgo de muerte súbita / Juan Ramón Gimeno Blanes ; dirección William J. McKenna, Mariano Valdés Chávarri.

Tesis-Universidad de Murcia.MEDICINA ESPINARDO. DEPOSITO. MU-Tesis 744.Consulte la tesis en: BCA. GENERAL. ARCHIVO UNIVERSITARIO. T.M. 2584

Electrocardiographic fragmented activity (I): physiological meaning of multivariate signal decompositions

Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection

Electrocardiographic fragmented activity (II): a machine learning approach to detection

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

Sex-related differences in cardiomyopathies

Cardiomyopathies (CMPs) are a heterogeneous group of heart muscle diseases with several different phenotypes defined as myocardial disorders in which the heart muscle is structurally and functionally abnormal in the absence of coronary artery disease, hypertension, valvular heart disease and congenital heart disease sufficient to explain the observed myocardial abnormality. CMPs can be classified into one of the following, i.e. hypertrophic CMP (HCM), dilated CMP (DCM), arrhythmogenic right ventricular CMP (ARVC), restrictive CMP (RCM), and unclassified CMPs. Although an increasing number of CMPs are now recognized to have a genetic basis, single mutations are associated with phenotypic variability and may cause not only a specific CMP, but also several different CMPs. Recently, it has become evident that, along with environmental interactions, age and sex may affect the penetrance of disease genes thus determining the phenotypic expression of CMPs. Noteworthy, an increasing body of data indicates that sex plays an important role in various forms of CMPs. The mode of inheritance may affect the sex-related occurrence of CMPs. Also, sex is a relevant determinant of the clinical manifestation of CMPs, and sex-related characteristics can be found in all forms. Sex-specific aspects of clinical disease expression as well as potential modes of inheritance should be therefore taken into proper consideration in order to improve the diagnostic work-up and treatment strategy of CMPs in both sexes

Phenotype and prognostic correlations of the converter region mutations affecting the β myosin heavy chain

Objectives: The prognostic value of genetic studies in cardiomyopathies is still controversial. Our objective was to evaluate the outcome of patients with cardiomyopathy with mutations in the converter domain of β myosin heavy chain (MYH7). Methods: Clinical characteristics and survival of 117 affected members with mutations in the converter domain of MYH7 were compared with 409 patients described in the literature with mutations in the same region. Results: Twenty-five mutations were evaluated (9 in our families including 3 novel (Ile730Asn, Asp717Gly and Arg719Pro)). Clinical diagnoses were hypertrophic (n=407), dilated (n=15), non-compaction (n=4) and restrictive (n=5) cardiomyopathies, unspecified cardiomyopathy (n=11), sudden death (n=50) and 35 healthy carriers. One hundred eighty-four had events (cardiovascular death or transplant). Median event-free survival was 50±2 years in our patients and 53±3 years in the literature (p=0.27). There were significant differences in the outcome between mutation: Ile736Thr had fewer events than other mutations in the region (p=0.01), while Arg719Gln (p<0.01) had reduced event-free survival. Conclusions: Mutations in the converter region are generally associated with adverse prognosis although there are differences between mutations. The identification of a mutation in this particular region provides important prognostic information that should be considered in the clinical management of affected patients