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

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. Here, we scrutinized the long-term robustness properties of three nonlinear methods for HRV characterization, namely, the Multiscale Entropy (MSE), the Multiscale Time Irreversibility (MTI), and the Multifractal Spectrum (MFS). These indices were selected because all of them have been theoretically designed to take into account the multiple time scales inherent in healthy and pathological cardiac dynamics, and they have been analyzed so far when monitoring up to 24 h of ECG signals, corresponding to about 20 time scales. We analyzed them in 7-day Holter recordings from two data sets, namely, patients with Atrial Fibrillation and with Congestive Heart Failure, by reaching up to 100 time scales. In addition, a new comparison procedure is proposed to statistically compare the poblational multiscale representations in different patient or processing conditions, in terms of the non-parametric estimation of confidence intervals for the averaged median differences. Our results show that variance reduction is actually obtained in the multiscale estimators. The MSE (MTI) exhibited the lowest (largest) bias and variance at large scales, whereas all the methods exhibited a consistent description of the large-scale processes in terms of multiscale index robustness. In all the methods, the used algorithms could turn to give some inconsistency in the multiscale profile, which was checked not to be due to the presence of artifacts, but rather with unclear origin. The reduction in standard error for several-day recordings compared to one-day recordings was more evident in MSE, whereas bias was more patently present in MFS. Our results pave the way of these techniques towards their use, with improved algorithmic implementations and nonparametric statistical tests, in long-term cardiac Holter monitoring scenarios.

Atrial fibrillation frequency tracking in ambulatory ECG signals: The significance of signal quality assessment

An approach to atrial fibrillation (AF) frequency tracking in long-term ambulatory ECG recordings is presented, comprising f-wave extraction, dominant atrial frequency (DAF) tracking, and signal quality assessment. Since poor signal quality is commonly encountered in ambulatory monitoring, a recently proposed index is employed to assess f-wave signal quality in a database containing 38 patients with permanent AF. The index ensures that DAF outliers, typically associated with poor-quality segments, are excluded from further analysis. 40% of all 5-s signal segments were excluded from the database due to poor quality. The exclusion of DAF outliers significantly reduces the standard deviation of the frequency estimates (p≤0.01), allowing more reliable evaluation of the difference between day- and night-time DAF. The results show that signal quality assessment plays a central role in DAF tracking, and therefore should be employed in ambulatory monitoring.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland⁻Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

Gabor frames for classification of paroxysmal and persistent atrial fibrillation episodes

In this study, we propose a new classification method for early differentiation of paroxysmal and persistent atrial fibrillation episodes, i.e. those which spontaneously or with external intervention will return to sinus rhythm within 7 days of onset from the ones where the arrhythmia is sustained for more than 7 days. Today, clinicians provide patients classification once the course of the arrhythmia has been disclosed. This classification problem is dealt with in this study. We study a sparse representation of surface electrocardiogram signals by means of Gabor frames and afterwards we apply a linear discriminant analysis. Thus, we provide an early discrimination, obtaining promising performances on a heterogeneous cohort of patients in terms of pharmacological treatment and state of progression of the arrhythmia: 95% sensitivity, 82% specificity, 89% accuracy. In this manner, the proposed method can help clinicians to choose the most appropriate treatment using the electrocardiogram, which is a widely available and non-invasive technique. This early differentiation is clinically highly significant in order to choose optimal patients who may undergo catheter ablation with higher success rates.