Machine learning based detection of T-wave alternans in real ambulatory conditions.

Background And Objective: T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis.

Machine Learning approach for TWA detection relying on ensemble data design.

Background And Objective: T-wave alternans (TWA) is a fluctuation of the ST-T complex of the surface electrocardiogram (ECG) on an every-other-beat basis. It has been shown to be clinically helpful for sudden cardiac death stratification, though the lack of a gold standard to benchmark detection methods limits its application and impairs the development of alternative techniques. In this work, a novel approach based on machine learning for TWA detection is proposed.

Autonomic Nervous System and Recall Modeling in Audiovisual Emotion-Mediated Advertising Using Partial Least Squares-Path Modeling.

Interest in improving advertisement impact on potential consumers has increased recently. One well-known strategy is to use emotion-based advertisement. In this approach, an emotional link with consumers is created, aiming to enhance the memorization process.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials.

During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans.

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial-temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay.

Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population.

The primary objective of our research was to compare the performance of data analysis to predict vitamin D deficiency using three different regression approaches and to evaluate the usefulness of incorporating machine learning algorithms into the data analysis in a clinical setting. We included 221 patients from our hypertension unit, whose data were collected from electronic records dated between 2006 and 2017. We used classical stepwise logistic regression, and two machine learning methods [least absolute shrinkage and selection operator (LASSO) and elastic net].

Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events.

Appropriate management of hypertensive patients relies on the accurate identification of clinically relevant features. However, traditional statistical methods may ignore important information in datasets or overlook possible interactions among features. Machine learning may improve the prediction accuracy and interpretability of regression models by identifying the most relevant features in hypertensive patients.

On the Robustness of Multiscale Indices for Long-Term Monitoring in Cardiac Signals.

The identification of patients with increased risk of Sudden Cardiac Death (SCD) has been widely studied during recent decades, and several quantitative measurements have been proposed from the analysis of the electrocardiogram (ECG) stored in 1-day Holter recordings. Indices based on nonlinear dynamics of Heart Rate Variability (HRV) have shown to convey predictive information in terms of factors related with the cardiac regulation by the autonomous nervous system, and among them, multiscale methods aim to provide more complete descriptions than single-scale based measures. However, there is limited knowledge on the suitability of nonlinear measurements to characterize the cardiac dynamics in current long-term monitoring scenarios of several days.

[Prevalence and clinical features of non-alcoholic steatohepatitis in a hypertensive population].

Objective: Non-alcoholic fatty liver is a chronic liver disease in which fat is deposited in the liver, causing an inflammation called non-alcoholic steatohepatitis (NASH), and fibrosis. NASH is associated with metabolic syndrome (MS) and other cardiovascular risk factors. The aim of this study was to analyse the epidemiological features of NASH within a hypertensive population, with a high prevalence of MS, and to determine the features related to NASH.

Lack of improvement in autonomic cardiac tone after sacubitril/valsartan at lower than target doses.

Autonomic regulation plays a role in the progression of heart failure with reduced ejection fraction (HrEF).Twenty-one HFrEF patients, 60.8 ± 13.

Full Band Spectra Analysis of Gait Acceleration Signals for Peripheral Arterial Disease Patients.

Peripheral arterial disease (PAD) is an artherosclerotic occlusive disorder of distal arteries, which can give rise to the intermittent claudication (IC) phenomenon, i.e., limb pain and necessity to stop.

Cardiovascular risk assessment in prediabetic patients in a hypertensive population: The role of cystatin C.

Background: The aim of our study was to determine whether prediabetes increases cardiovascular (CV) risk compared to the non-prediabetic patients in our hypertensive population. Once this was achieved, the objective was to identify relevant CV prognostic features among prediabetic individuals.

Methods: We included hypertensive 1652 patients.

Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population.

Calculating the estimated glomerular filtration rate (eGFR) using creatinine-based equations may underestimate cardiovascular risk. Cystatin C-based eGFR may be a stronger prognostic biomarker than creatinine-based eGFR when assessing cardiovascular outcomes and mortality. Our aim was to determine whether levels of serum cystatin C, as an estimator of GFR, had a higher predictive value than creatinine-based eGFR for incident cardiovascular disease among hypertensive patients.

Benchmarking of a T-wave alternans detection method based on empirical mode decomposition.

Background And Objective: T-wave alternans (TWA) is a fluctuation of the ST-T complex occurring on an every-other-beat basis of the surface electrocardiogram (ECG). It has been shown to be an informative risk stratifier for sudden cardiac death, though the lack of gold standard to benchmark detection methods has promoted the use of synthetic signals. This work proposes a novel signal model to study the performance of a TWA detection.

Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning.

Accurate identification of Perinatal Hypoxia from visual inspection of Fetal Heart Rate (FHR) has been shown to have limitations. An automated signal processing method for this purpose needs to deal with time series of different lengths, recording interruptions, and poor quality signal conditions. We propose a new method, robust to those issues, for automated detection of perinatal hypoxia by analyzing the FHR during labor.

On the Influence of Heart Rate and Coupling Interval Prematurity on Heart Rate Turbulence.

Objective: Heart rate turbulence (HRT) has been successfully explored for cardiac risk stratification. While HRT is known to be influenced by the heart rate (HR) and the coupling interval (CI), nonconcordant results have been reported on how the CI influences HRT. The purpose of this study is to investigate HRT changes in terms of CI and HR by means of an especially designed protocol.

Long-term characterization of persistent atrial fibrillation: wave morphology, frequency, and irregularity analysis.

Short-term properties of atrial fibrillation (AF) frequency, f-wave morphology, and irregularity parameters have been thoroughly studied, but not long-term properties. In the present work, f-wave morphology is characterized by principal component analysis, introducing a novel temporal parameter defined by the cumulative normalized variance of the three largest principal components (r3). Based on 7-day recordings from nine patients with stable chronic heart failure and persistent AF, long-term properties were studied in terms of r3 AF frequency, and sample entropy (SampEn).

Nonparametric signal processing validation in T-wave alternans detection and estimation.

Although a number of methods have been proposed for T-Wave Alternans (TWA) detection and estimation, their performance strongly depends on their signal processing stages and on their free parameters tuning. The dependence of the system quality with respect to the main signal processing stages in TWA algorithms has not yet been studied. This study seeks to optimize the final performance of the system by successive comparisons of pairs of TWA analysis systems, with one single processing difference between them.

Heart rate variability and phantom pain in male amputees: application of linear and nonlinear methods.

Phantom-limb pain (PLP) is a phenomenon that may appear among people with amputation. Some studies reveal that 70% of people with amputation experience PLP years postamputation. There is a lack of scientific evidence about the cause of PLP.

Prognostic significance of long-period heart rate rhythms in chronic heart failure.

Background: Abnormalities in autonomic control are a feature of neuroendocrine activation in HF and are responsible for dysregulation of biological rhythms. The purpose was to investigate the presence and the prognostic significance of long-period heart rate (HR) rhythms in heart failure (HF) patients.

Methods And Results: In the study, 92 HF patients were enrolled (age 53 ± 14 years and left ventricular ejection fraction [LVEF] 37 ± 10%).

Short-term variability of heart rate turbulence in chronic heart failure.

Background: Heart rate turbulence (HRT) is associated with risk in chronic heart failure (CHF). The objective of this study was to assess the short-term variability of HRT and to compare the diagnostic yield of 7-day (7DH) versus 24-hour (1DH) Holter monitoring for calculating HRT in a CHF population.

Methods And Results: Forty-nine consecutive patients with CHF were studied.

Comparison of detection of arrhythmias in patients with chronic heart failure secondary to non-ischemic versus ischemic cardiomyopathy by 1 versus 7-day holter monitoring.

The purpose of this study was to compare the diagnostic sensitivity of 1-day Holter monitoring versus 7-day Holter monitoring (7DH) to detect atrial and ventricular arrhythmias in a population of stable patients with chronic heart failure and left ventricular dysfunction. Sixty-three consecutive stable patients with chronic heart failure with left ventricular ejection fractions < or =50% were included. Blood samples were obtained, the Minnesota Living With Heart Failure Questionnaire was administered, and echocardiography, 6-minute walk tests, and 7DH were performed at enrollment.

Fundamental frequency and regularity of cardiac electrograms with Fourier organization analysis.

Dominant frequency analysis (DFA) and organization analysis (OA) of cardiac electrograms (EGMs) aims to establish clinical targets for cardiac arrhythmia ablation. However, these previous spectral descriptions of the EGM have often discarded relevant information in the spectrum, such as the harmonic structure or the spectral envelope. We propose a fully automated algorithm for estimating the spectral features in EGM recordings.

Heart rate variability on 7-day Holter monitoring using a bootstrap rhythmometric procedure.

Heart rate variability (HRV) markers have been widely used to characterize the autonomous regulation state of the heart from 24-h Holter monitoring, but long-term evolution of HRV indexes is mostly unknown. A dataset of 7-day Holter recordings of 22 patients with congestive heart failure was studied. A rhythmometric procedure was designed to characterize the infradian, circadian, and ultradian components for each patient, as well as circadian and ultradian fluctuations.