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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. At inclusion, 7DH was performed to evaluate the variability of HRT parameters. For categorized analyses, turbulence onset (TO) >=0% and turbulence slope (TS) <=2.5 ms/RR were defined as abnormal, and patients were classified into subgroups based on the number of abnormal HRT parameters.The cumulative percentage of patients with calculable HRT increased from 69.4% with 1DH to 93.9% with 7DH. The intraclass correlation coefficients across the 7-day monitoring were 0.81 (95% confidence interval [CI] 0.70-0.89) for TO and 0.90 (95% CI 0.84-0.95) for TS. When comparing 2 randomly selected days, TO and TS values were similar (P > .1) and showed a strong correlation (TO: r = 0.79; TS: r = 0.84: P < .001). Bland-Altman plots showed a mean difference of 0.31% (95% CI -0.07 to 0.70) for TO and 0.44 ms/RR (95% CI -1.37 to 0.48) for TS. In contrast, categorized analyses showed that up to 16% of patients changed their HRT subgroup score from day 1 to day 2 of comparison. CONCLUSIONS: In this population, 7DH significantly increased the percentage of patients with calculable HRT parameters. The short-term variability of the quantitative HRT values was good, but when patients were categorized into the established HRT subgroups, the concordance was suboptimal. [ABSTRACT FROM AUTHOR]

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. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds., Methods: In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events., Results: We train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods., Conclusions: We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores., Competing Interests: Declaration of Competing Interest The authors of the work entitled “Machine learning based detection of T-wave alternans in real ambulatory conditions” declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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. Additionally, a complete experimental setup is presented for TWA detection methods benchmarking., Methods: The proposed experimental setup is based on the use of open-source databases to enable experiment replication and the use of real ECG signals with added TWA episodes. Also, intra-patient overfitting and class imbalance have been carefully avoided. The Spectral Method (SM), the Modified Moving Average Method (MMA), and the Time Domain Method (TM) are used to obtain input features to the Machine Learning (ML) algorithms, namely, K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron., Results: There were not found large differences in the performance of the different ML algorithms. Decision Trees showed the best overall performance (accuracy 0.88 ± 0.04 , precision 0.89 ± 0.05 , Recall 0.90 ± 0.05 , F1 score 0.89 ± 0.03 ). Compared to the SM (accuracy 0.79, precision 0.93, Recall 0.64, F1 score 0.76) there was an improvement in every metric except for the precision., Conclusions: In this work, a realistic database to test the presence of TWA using ML algorithms was assembled. The ML algorithms overall outperformed the SM used as a gold standard. Learning from data to identify alternans elicits a substantial detection growth at the expense of a small increment of the false alarm., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s).)

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. In recent years, Neuromarketing techniques have allowed us to obtain more objective information on this process. However, the role of the autonomic nervous system (ANS) in the memorization process using emotional advertisement still needs further research. In this work, we propose the use of two physiological signals, namely, an electrocardiogram (heart rate variability, HRV) and electrodermal activity (EDA), to obtain indices assessing the ANS. We measured these signals in 43 subjects during the observation of six different spots, each conveying a different emotion (rational, disgust, anger, surprise, and sadness). After observing the spots, subjects were asked to answer a questionnaire to measure the spontaneous and induced recall. We propose the use of a statistical data-driven model based on Partial Least Squares-Path Modeling (PSL-PM), which allows us to incorporate contextual knowledge by defining a relational graph of unobservable variables (latent variables, LV), which are, in turn, estimated by measured variables (indices of the ANS). We defined four LVs, namely, sympathetic, vagal, ANS , and recall . Sympathetic and vagal are connected to the ANS , the latter being a measure of recall, estimated from a questionnaire. The model is then fitted to the data. Results showed that vagal activity (described by HRV indices) is the most critical factor to describe ANS activity; they are inversely related except for the spot, which is mainly rational. The model captured a moderate-to-high variability of ANS behavior, ranging from 38% up to 64% of the explained variance of the ANS. However, it can explain at most 11% of the recall score of the subjects. The proposed approach allows for the easy inclusion of more physiological measurements and provides an easy-to-interpret model of the ANS response to emotional advertisement., (Copyright © 2020 Barquero-Pérez, Cámara-Vázquez, Vadillo-Valderrama and Goya-Esteban.)

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. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.

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. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial-temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial-temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial-temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.

Aim: 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. Methods: 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]. We assessed the performance of these three algorithms in terms of sensitivity, specificity, misclassification error, and area under the curve (AUC). Results: LASSO and elastic net regression performed better than logistic regression in terms of AUC, which was significantly better in both penalized methods, with AUC = 0.76 and AUC = 0.74 for elastic net and LASSO, respectively, than in logistic regression, with AUC = 0.64. In terms of misclassification rate, elastic net (18%) outperformed LASSO (22%) and logistic regression (25%). Conclusion: Compared with a classical logistic regression approach, penalized methods were found to have better performance in predicting vitamin D deficiency. The use of machine learning algorithms such as LASSO and elastic net may significantly improve the prediction of vitamin D deficiency in a hypertensive obese population.

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. We sought the most relevant features for prediction of cardiovascular (CV) events in a hypertensive population. We used the penalized regression models least absolute shrinkage and selection operator (LASSO) and elastic net (EN) to obtain the most parsimonious and accurate models. The clinical parameters and laboratory biomarkers were collected from the clinical records of 1,471 patients receiving care at Mostoles University Hospital. The outcome was the development of major adverse CV events. Cox proportional hazards regression was performed alone and with penalized regression analyses (LASSO and EN), producing three models. The modeling was performed using 10-fold cross-validation to fit the penalized models. The three predictive models were compared and statistically analyzed to assess their classification accuracy, sensitivity, specificity, discriminative power, and calibration accuracy. The standard Cox model identified five relevant features, while LASSO and EN identified only three (age, LDL cholesterol, and kidney function). The accuracies of the models (prediction vs. observation) were 0.767 (Cox model), 0.754 (LASSO), and 0.764 (EN), and the areas under the curve were 0.694, 0.670, and 0.673, respectively. However, pairwise comparison of performance yielded no statistically significant differences. All three calibration curves showed close agreement between the predicted and observed probabilities of the development of a CV event. Although the performance was similar for all three models, both penalized regression analyses produced models with good fit and fewer features than the Cox regression predictive model but with the same accuracy. This case study of predictive models using penalized regression analyses shows that penalized regularization techniques can provide predictive models for CV risk assessment that are parsimonious, highly interpretable, and generalizable and that have good fit. For clinicians, a parsimonious model can be useful where available data are limited, as such a model can offer a simple but efficient way to model the impact of the different features on the prediction of CV events. Management of these features may lower the risk for a CV event. Graphical Abstract In a clinical setting, with numerous biological and laboratory features and incomplete datasets, traditional statistical methods may ignore important information and overlook possible interactions among features. Our aim was to identify the most relevant features to predict cardiovascular events in a hypertensive population, using three different regression approaches for feature selection, to improve the prediction accuracy and interpretability of regression models by identifying the relevant features in these patients.

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.

Autonomic regulation plays a role in the progression of heart failure with reduced ejection fraction (HrEF).Twenty-one HFrEF patients, 60.8 ± 13.1 years, receiving angiotensin inhibition, were replaced by angiotensin receptor-neprilysin inhibitor (ARNI). A 24-hour Holter recording was performed before and after 3 months of the maximum tolerated dose of ARNi. We evaluated changes in autonomic tone using heart rate variability (SDNN, rMSSD, pNN50, LF, HF, LF/HF, α1, α2), and heart rate turbulence (TO and TS). ARNI was up-titrated to a maximum daily dose of 190 ± 102 mg, 47.5% of the target dose. ARNI therapy was not associated with any improvement in any of the parameters related with heart rate variability or heart rate turbulence (p > 0.05 for all). ARNI use at lower than target doses did not improve autonomic cardiac tone as evaluated by 24-hour Holter monitoring., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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. The primary outcome was a composite of incident CV events: cardiovascular death, stroke, heart failure and myocardial infarction. We performed a Cox proportional hazard regression to assess the CV risk of prediabetic patients compared to non-prediabetic and to produce a survival model in the prediabetic cohort., Results: The risk of developing a CV event was higher in the prediabetic cohort than in the non-prediabetic cohort, with a hazard ratio (HR) = 1.61, 95% CI 1.01-2.54, p = 0.04. Our Cox proportional hazard model selected age (HR = 1.04, 95% CI 1.02-1.07, p < 0.001) and cystatin C (HR = 2.4, 95% CI 1.26-4.22, p = 0.01) as the most relevant prognostic features in our prediabetic patients., Conclusions: Prediabetes was associated with an increased risk of CV events, when compared with the non-prediabetic patients. Age and cystatin C were found as significant risk factors for CV events in the prediabetic cohort., (Copyright © 2018 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

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. PAD patients with IC have altered their gait, increasing the fall risk. Several gait analysis works have studied acceleration signals (from sensors) to characterize the gait. One common technique is spectral analysis. However, this approach mainly uses dominant frequency ( f d ) to characterize gait patterns, and in a narrow spectral band, disregarding the full spectra information. We propose to use a full band spectral analysis (up to 15 Hz) and the fundamental frequency ( f 0 ) in order to completely characterize gait for both control subjects and PAD patients. Acceleration gait signals were recorded using an acquisition equipment consisting of four wireless sensor nodes located at ankle and hip height on both sides. Subjects had to walk, free-fashion, up to 10 min. The analysis of the periodicity of the gait acceleration signals, showed that f 0 is statistically higher ( p < 0.05) in control subjects (0.9743 ± 0.0716) than in PAD patients (0.8748 ± 0.0438). Moreover, the spectral envelope showed that, in controls, the power spectral density distribution is higher than in PAD patients, and that the power concentration is hither around the f d . In conclusion, full spectra analysis allowed to better characterize gait in PAD patients than classical spectral analysis. It allowed to better discriminate PAD patients and control subjects, and it also showed promising results to assess severity of PAD.

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. Additionally, the methodological validation of a denoising technique based on empirical mode decomposition (EMD), which is used here along with the spectral method, is also tackled., Methods: The proposed test bed system is based on the following guidelines: (1) use of open source databases to enable experimental replication; (2) use of real ECG signals and physiological noise; (3) inclusion of randomized TWA episodes. Both sensitivity (Se) and specificity (Sp) are separately analyzed. Also a nonparametric hypothesis test, based on Bootstrap resampling, is used to determine whether the presence of the EMD block actually improves the performance., Results: The results show an outstanding specificity when the EMD block is used, even in very noisy conditions (0.96 compared to 0.72 for SNR = 8 dB), being always superior than that of the conventional SM alone. Regarding the sensitivity, using the EMD method also outperforms in noisy conditions (0.57 compared to 0.46 for SNR=8 dB), while it decreases in noiseless conditions., Conclusions: The proposed test setting designed to analyze the performance guarantees that the actual physiological variability of the cardiac system is reproduced. The use of the EMD-based block in noisy environment enables the identification of most patients with fatal arrhythmias., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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. Our system consists of several stages: (a) time series segmentation; (b) feature extraction from FHR signals, including raw time series, moments, and usual heart rate variability indices; (c) similarity calculation with Normalized Compression Distance, which is the key element for dealing with FHR time series; and (d) a simple classification algorithm for providing the hypoxia detection. We analyzed the proposed system using a database with 32 fetal records (15 controls). Time and frequency domain and moment features had similar performance identifying fetuses with hypoxia. The final system, using the third central moment of the FHR, yielded 92% sensitivity and 85% specificity at 3 h before delivery. Best predictions were obtained in time intervals more distant from delivery, i.e., 4-3 h and 3-2 h.

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., Methods: A dataset was acquired from 11 patients with structurally normal hearts for which CI was altered by different pacing trains and HR by isoproterenol during electrophysiological study (EPS). The protocol was designed so that, first, the effect of HR changes on HRT and, second, the combined effect of HR and CI could be explored. As a complement to the EPS dataset, a database of 24-h Holters from 61 acute myocardial infarction (AMI) patients was studied for the purpose of assessing risk. Data analysis was performed by using different nonlinear ridge regression models, and the relevance of model variables was assessed using resampling methods. The EPS subjects, with and without isoproterenol, were analyzed separately., Results: The proposed nonlinear regression models were found to account for the influence of HR and CI on HRT, both in patients undergoing EPS without isoproterenol and in low-risk AMI patients, whereas this influence was absent in high-risk AMI patients. Moreover, model coefficients related to CI were not statistically significant, p > 0.05, on EPS subjects with isoproterenol., Conclusion: The observed relationship between CI and HRT, being in agreement with the baroreflex hypothesis, was statistically significant ( ), when decoupling the effect of HR and normalizing the CI by the HR., Significance: The results of this study can help to provide new risk indicators that take into account physiological influence on HRT, as well as to model how this influence changes in different cardiac conditions.

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). The main result of the present study is that detection of circadian rhythms depends on the parameter considered: rhythms were found in six (r3, SampEn) and five (AF frequency) patients, but not always in the same patient. Another important result is that circadian rhythms detected in 7-day recordings could not always be detected in 24-h periods, thus shedding new light on the results in previous studies which all were based on 24-h recordings. Infradian rhythms were found in four (r3, SampEn) and one (AF frequency) patients.

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. It has been hypothesized that the autonomic nervous system (ANS) could be involved in the mechanism that triggers PLP, but this hypothesis remains unclear. The aim of this study was to correlate ANS function, through heart rate variability (HRV) analysis, with PLP in adult males with amputation. The study population comprised 35 subjects, with 27 reporting PLP often or always. The rest of the subjects did not report any PLP. In order to calculate linear and nonlinear parameters of HRV, all subjects underwent 10 min of resting heart rate monitoring. The study did not find correlations between HRV parameters and PLP. Most of the subjects showed decreased values in linear parameters of HRV while nonlinear values were normal. HRV is not implicated in PLP. Linear and nonlinear methods for HRV analysis might reflect different physiological phenomena; while linear values place people with amputation at cardiovascular risk, nonlinear values indicate normality.

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%). A rhythmometric analysis was used to assess the HR rhythms in 7-days (7D) Holter recordings. Rhythms properties were quantified by mesor and amplitude, in beats/min and by acrophase, in hours. Cardiac death or HF decompensation were registered. All patients had 24-h rhythm, 61 patients (77%) had 8-h rhythm, and 66 patients (83%) had 7D rhythm. Twelve patients (15%) experienced events. Among rhythm parameters only 7D median amplitude was different between patients with or without events: 1.1 beats/min [0.5-1.5] vs. 2.0 beats/min [0.0-3.9], P=0.049 respectively. After multivariate adjustment, LVEF (per 1%, hazard ratio 0.92, 95% confidence interval (CI) 0.87 to 0.98, P=0.01), N-terminal portion of pro-natriuretic hormone type B (per 100 pg/ml, hazard ratio 1.036, 95% CI 1.005-1.069, P=0.022), and 7D amplitude of the HR ≤1.71 beats/min (hazard ratio 5.4, 95% CI 1.2-34.4, P=0.047) were independent predictors of events., Conclusions: A 7D HR rhythm is present in most patients with HF, and has prognostic significance.

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. The mean ejection fraction was 35.8 +/- 9.8%, and the mean age was 55.5 +/- 13.9 years. Seven-day Holter monitoring did not significantly increase the detection of nonsustained atrial tachycardia or atrial fibrillation. In contrast, the incidence of nonsustained ventricular tachycardia increased in nonischemic patients from 35.1% on day 1 to 54.1% on day 7 (p = 0.01). In ischemic patients, the sensitivity increased from 11.5% to 46.2% (p = 0.004). Two patients without nonsustained ventricular tachycardia on day 1 had episodes of 13 and 16 beats on days 3 and 6 of monitoring. In patients with left ventricular ejection fractions >35% and N-terminal-pro-brain natriuretic peptide levels <1,000 pg/ml, no episodes of nonsustained ventricular tachycardia were detected on day 1 in nonischemic and ischemic patients, but 7DH detected 3 new patients in each group. In conclusion, 7DH clearly improves the detection and allows a better characterization of ventricular arrhythmic episodes but seems to be less useful for supraventricular events., (2010 Elsevier Inc. All rights reserved.)

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. Furthermore, a bootstrap test yielded automatically the rhythmometric model for each patient. We analyzed the temporal evolution of relevant time-domain (AVNN, SDNN, and NN50), frequency-domain (LF, HF, HFn, and LF/HF), and nonlinear (alpha(1) and SampEn) HRV indexes. Circadian components were the most significant for all HRV indexes, but the infradian ones were also strongly present in NN50, HFn, LF/HF, alpha(1), and SampEn indexes. Among ultradian components that one corresponding to 12 h, was the most relevant. Long-term monitoring of HRV conveys new potentially relevant rhythmometric information, which can be analyzed by using the proposed automatic procedure.