3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads.

The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the PhysioNet/Computing in Cardiology Challenge 2021. The training set is a public database of 88,253 twelve-lead ECG recordings lasting from 6 s to 60 s. Each ECG recording has one or more diagnostic labels.

Modifications in Electrocardiographic and Vectordardiographic Morphological Parameters in Elderly Males as Result of Cardiovascular Diseases and Diabetes Mellitus.

Purpose: Morphological electrocardiographic and vectorcardiographic features have been used in the detection of cardiovascular diseases and prediction of the risk of cardiac death for a long time. The objective of the current study was to investigate the morphological electrocardiographic modifications in the presence of cardiovascular diseases and diabetes mellitus in an elderly male population, most of them with multiple comorbidities.

Methods: A database of ECG recordings from the Italian Longitudinal Study on Aging (ILSA-CNR), created to evaluate physiological and pathological modifications related to aging, was considered.

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics.

The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set.

Personal Verification/Identification via Analysis of the Peripheral ECG Leads: Influence of the Personal Health Status on the Accuracy.

Traditional means for identity validation (PIN codes, passwords), and physiological and behavioral biometric characteristics (fingerprint, iris, and speech) are susceptible to hacker attacks and/or falsification. This paper presents a method for person verification/identification based on correlation of present-to-previous limb ECG leads: I (r I), II (r II), calculated from them first principal ECG component (r PCA), linear and nonlinear combinations between r I, r II, and r PCA. For the verification task, the one-to-one scenario is applied and threshold values for r I, r II, and r PCA and their combinations are derived.

A review on electrocardiographic changes in diabetic patients.

Prevalence of diabetes mellitus (DM) is progressively increasing, contributing to a parallel increase in cardiovascular morbidity and mortality, and more than doubling the incidence of sudden cardiac death (SCD). Certain electrocardiographic (ECG) characteristics, such as alternans of the T wave (TWA), heart rate variability (HRV) and dispersion of the QT interval, have been found to be predictive of the risk of SCD. This review focuses on ECG changes that could be found in diabetics and their potential implication for SCD risk.

QT dispersion in the elderly. The ILSA Study.

Background And Aims: In the last few years there has been active interest in the study of QT interval dispersion (QT-d) calculated from the 12-lead ECG, as a non-invasive method of investigating the homogeneity of ventricular recovery time. The aim of this study was to evaluate QT interval dispersion, analyzing in particular gender and age relationships. Discussion of its electrophysiologic and clinic meaning is still open; moreover, the method must be standardized and normal values should be clearly defined.

An interactive framework for an analysis of ECG signals.

In this study, we introduce and discuss a development of a highly interactive and user-friendly environment for an ECG signal analysis. The underlying neural architecture being a crux of this environment comes in the form of a self-organizing map. This map helps discover a structure in a set of ECG patterns and visualize a topology of the data.