Beat-to-beat electrocardiographic analysis of ventricular repolarization variability in patients after myocardial infarction

Arini, Pedro David; Valverde, Esteban

doi:10.1016/j.jelectrocard.2015.12.003

Cited by 17 publications

(3 citation statements)

References 32 publications

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“…Los monitores de ECG de una sola derivación se utilizan frecuentemente debido a su naturaleza altamente productiva, corto tiempo de ejecución y bajo costo [42]. Sin embargo, los ECG de una derivación no pueden capturar toda la información debido a la gran diversidad de características de las CVD que pueden causar diagnósticos equivocados [43].…”

Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

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2023

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El aumento de la demanda en los distintos sistemas eléctricos de potencia (SEP) tiene un impacto negativo en la estabilidad de la tensión, la confiabilidad y la calidad del suministro eléctrico. El perfil de tensión disminuye cuando las unidades de generación no son capaces de suministrar potencia reactiva al sistema eléctrico en los momentos que se requiere. Con el desarrollo de la electrónica de potencia y los complejos sistemas de control, se han podido introducir dispositivos de sistemas flexibles de transmisión de corriente alterna (FACTS, del inglés Flexible Alternating Current Transmission System). En este artículo se analiza en detalle el impacto que genera la introducción de un tipo de FACTS que permite la compensación de potencia reactiva en el SEP. Además, se analizará una metodología para decidir la capacidad del compensador síncrono estático (STATCOM, del inglés Static Synchronous Compensator) y su ubicación óptima con la ejecución de flujos de potencia continuos (FPC). Finalmente, se estudiará el impacto positivo de la instalación de un control estabilizador de potencia (PSS, del inglés Power System Stabilizer) para asegurar la estabilidad de tensión en el SEP. Este artículo se desarrolla utilizando el sistema base IEEE de 14 barras bajo dos modelos matemáticos para el cálculo del flujo de potencia desarrollados en el software Matlab, que son: i) utilizando las ecuaciones de balance de potencia y ii) Newton Raphson con el toolbox de MATLAB (PSAT, del inglés Power System Analysis Toolbox).Increased demand in the different electrical power systems (EPS) has a negative impact in voltage stability, reliability and quality of the power supply. Voltage profile is reduced when generation units are not capable of supplying reactive power to the EPS at the times it is required. With the development of power electronics and complex control systems, flexible alternating current transmission system (FACTS) devices have been introduced. In this article, the impact of the introduction of a type of FACTS that allows reactive power compensation in the EPS is analyzed in detail. Furthermore, a methodology to decide the capacity of the Static Synchronous Compensator (STATCOM) and its optimal location with the execution of continuous power flows (CPF) will be analyzed. Finally, the positive impact of installing a Power System Stabilizer (PSS) control to ensure voltage stability in the EPS will be studied. This article is developed using the IEEE 14-bus base system under two mathematical models for power flow calculation developed in MATLAB software, which are: which are: i) through the power balance equations and ii ) Newton Raphson with the toolbox PSAT.

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Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

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2023

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Section: Enfermedades Cardiovasculares E Inteligencia Artificialunclassified

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Patiño

Medina

Bustillos

et al. 2023

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Cardiovascular diseases such as Acute Myocardial Infarction is one of the 3 leading causes of death in the world according to WHO data, in the same way cardiac arrhythmias are very common diseases today, such as atrial fibrillation. The ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, ANN, and CNN artificial and neural networks, added to tree-based models such as XGBoost, are of vital help in the prevention and control of heart disease. The present study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias and Acute Myocardial Infarctions respectively. The learning times and the percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the learning prediction.

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“…At present, the use of single-lead portable ECG monitors in daily monitoring is a trend due to its low instrumentation cost, highly efficient and comfort uses [4]. However, the time-varying dynamics and morphological characteristics of single-lead ECG signal show significant and complex variations for different patients with MI, under different area of MI and different degrees of myocardial damage.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

et al. 2019

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Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the singlelead ECG signals of MI. INDEX TERMS Electrocardiograph, myocardial infarction, sparse autoencoder, bagged decision tree, deep learning networks.

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Beat-to-beat electrocardiographic analysis of ventricular repolarization variability in patients after myocardial infarction

Cited by 17 publications

References 32 publications

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Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger

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