Montserrat Vallverdú

Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects

Multiscale entropy (MSE) was proposed to characterize complexity as a function of the time-scale factor tau. Despite its broad use, this technique suffers from two limitations: (1) the artificial MSE reduction due to the coarse graining procedure and (2) the introduction of spurious MSE oscillations due to the suboptimal procedure for the elimination of the fast temporal scales. We propose a refined MSE (RMSE), and we apply it to simulations and to 24-h Holter recordings of heart rate variability (HRV) obtained from healthy and aortic stenosis (AS) groups. The study showed that the refinement relevant to the elimination of the fast temporal scales was more helpful at short scales (spanning the range of short-term HRV oscillations), while that relevant to the procedure of coarse graining was more useful at large scales. In healthy subjects, during daytime, RMSE was smaller at short scales (i.e., tau =1-2) and larger at longer scales (i.e., tau =4-20) than during nighttime. In AS population, RMSE was smaller during daytime both at short and long time scales (i.e., tau = 1 -11) than during nighttime. RMSE was larger in healthy group than in AS population during both daytime (i.e., tau = 2 -9) and nighttime (i.e., tau = 2). RMSE overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.

Detrended Fluctuation Analysis of EEG as a Measure of Depth of Anesthesia

For several decades, a number of methods have been developed for the noninvasive assessment of the level of consciousness during general anesthesia. In this paper, detrended fluctuation analysis is used to study the scaling behavior of the electroencephalogram as a measure of the level of consciousness. Three indexes are proposed in order to characterize the patient state. Statistical analysis demonstrates that they allow significant discrimination between the awake, sedated and anesthetized states. Two of them present a good correlation with established indexes of depth of anesthesia. The scaling behavior has been found related to the depth of anesthesia and the methodology allows real-time implementation, which enables its application in monitoring devices

Heart rate turbulence predicts all-cause mortality and sudden death in congestive heart failure patients

BACKGROUND Abnormal heart rate turbulence (HRT) has been documented as a strong predictor of total mortality and sudden death in postinfarction patients, but data in patients with congestive heart failure (CHF) are limited. OBJECTIVE The aim of this study was to evaluate the prognostic significance of HRT for predicting mortality in CHF patients in New York Heart Association (NYHA) class II-III. METHODS In 651 CHF patients with sinus rhythm enrolled into the MUSIC (Muerte Subita en Insuficiencia Cardiaca) study, the standard HRT parameters turbulence onset (TO) and slope (TS), as well as HRT categories, were assessed for predicting total mortality and sudden death. RESULTS HRT was analyzable in 607 patients, mean age 63 years (434 male), 50% of ischemic etiology. During a median follow up of 44 months, 129 patients died, 52 from sudden death. Abnormal TS and HRT category 2 (HRT2) were independently associated with increased all-cause mortality (HR: 2.10, CI: 1.41 to 3.12, P <.001 and HR: 2.52, CI: 1.56 to 4.05, P <.001; respectively), sudden death (HR: 2.25, CI: 1.13 to 4.46, P = .021 for HRT2), and death due to heart failure progression (HR: 4.11, CI: 1.84 to 9.19, P <.001 for HRT2) after adjustment for clinical covariates in multivariate analysis. The prognostic value of TS for predicting total mortality was similar in various groups dichotomized by age, gender, NYHA class, left ventricular ejection fraction, and CHF etiology. TS was found to be predictive for total mortality only in patients with QRS > 120 ms. CONCLUSION HRT is a potent risk predictor for both heart failure and arrhythmic death in patients with class II and III CHF.

Mixed quantitative:qualitative modeling and simulation of the cardiovascular system

The cardiovascular system is composed of the hemodynamical system and the central nervous system (CNS) control. Whereas the structure and functioning of the hemodynamical system are well known and a number of quantitative models have already been developed that capture the behavior of the hemodynamical system fairly accurately, the CNS control is, at present, still not completely understood and no good deductive models exist that are able to describe the CNS control from physical and physiological principles. The use of qualitative methodologies may offer an interesting alternative to quantitative modeling approaches for inductively capturing the behavior of the CNS control. In this paper, a qualitative model of the CNS control of the cardiovascular system is developed by means of the fuzzy inductive reasoning (FIR) methodology. FIR is a fairly new modeling technique that is based on the general system problem solving (GSPS) methodology developed by G.J. Klir (Architecture of Systems Problem Solving, Plenum Press, New York, 1985). Previous investigations have demonstrated the applicability of this approach to modeling and simulating systems, the structure of which is partially or totally unknown. In this paper, five separate controller models for different control actuations are described that have been identified independently using the FIR methodology. Then the loop between the hemodynamical system, modeled by means of differential equations, and the CNS control, modeled in terms of five FIR models, is closed, in order to study the behavior of the cardiovascular system as a whole. The model described in this paper has been validated for a single patient only.

Dimensional analysis of HRV in hypertrophic cardiomyopathy patients

Hypertrophic cardiomyopathy (HCM) is an excessive thickening of the heart muscle in the absence of an apparent cause. This condition excludes individuals with high blood pressure or prolonged athletic training. It is characterized by left and/or right ventricular hypertrophy, which is usually asymmetric. It is a familial disease with autosomal dominant inheritance caused by mutations in the sarcomeric contractile protein gene [1]. The electrocardiogram (ECG) of those patients who have this pathology shows an abnormal electric signal due to the thickening of the heart and the loss of the normal alignment of heart muscle cells. Some H CM patients c an d evelop arrhythmias (ventricular tachycardia and atrial fibrillation), endocarditis, heart block, and also sudden cardiac death (SCD). In HCM patients there is an increased risk of premature death, which can occur with little or no warning. SCD can strike at any age [2]. However, stratification for sudden cardiac death on patients with HCM is highly difficult [3].

Spectral analysis of heart period variance (HPV) – a tool to stratify risk following myocardial infarction

The purpose of this study was to contribute to the improvement of stratification of post-myocardial infarction patients at increased risk of malignant ventricular arrhythmia (MVA). Power spectral analysis of heart period variability (HPV) was used as a non-invasive tool to assess cardiac autonomic control. Three groups were used: (1) post-myocardial infarction patients with MVA; (2) post-myocardial infarction patients without MVA; and (3) a control group without heart disease. Spectral analysis of HPV (AR model) was performed on four minute long RR-interval time series derived from consecutive hours of Holter ECG. Significant decrease of powers of mid-frequency (MF) (70-150 mHz) and high-frequency (HF) (150-450 mHz) spectral components of HPV was obtained in Group 1 as compared to Group 2 (p = 0.001 and p = 0.02, respectively). There were no significant differences between groups concerning the power of low frequency (LF) (10-70 mHz) component HPV, spectra of patients in Group 1 were dominated by a single low frequency spectral peak (with a central frequency of 37 mHz). The relative power was computed as the percentage of power in each of the above (HF, MF, LF) components related to the total spectral power. Highly significant differences (p = 0.04) were obtained between Group 1 and Group 2 concerning relative powers of MF and LF components as well as LF/MF ratio. The above method appeared to be highly sensitive in differentiating patients with increased risk of MVA.

Variability analysis of the respiratory volume based on non-linear prediction methods

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

Symbolic Dynamic Analysis of Relations Between Cardiac and Breathing Cycles in Patients on Weaning Trials

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

Multiscale Complexity Analysis of the Cardiac Control Identifies Asymptomatic and Symptomatic Patients in Long QT Syndrome Type 1

The study assesses complexity of the cardiac control directed to the sinus node and to ventricles in long QT syndrome type 1 (LQT1) patients with KCNQ1-A341V mutation. Complexity was assessed via refined multiscale entropy (RMSE) computed over the beat-to-beat variability series of heart period (HP) and QT interval. HP and QT interval were approximated respectively as the temporal distance between two consecutive R-wave peaks and between the R-wave apex and T-wave end. Both measures were automatically taken from 24-hour electrocardiographic Holter traces recorded during daily activities in non mutation carriers (NMCs, n = 14) and mutation carriers (MCs, n = 34) belonging to a South African LQT1 founder population. The MC group was divided into asymptomatic (ASYMP, n = 11) and symptomatic (SYMP, n = 23) patients according to the symptom severity. Analyses were carried out during daytime (DAY, from 2PM to 6PM) and nighttime (NIGHT, from 12PM to 4AM) off and on beta-adrenergic blockade (BBoff and BBon). We found that the complexity of the HP variability at short time scale was under vagal control, being significantly increased during NIGHT and BBon both in ASYMP and SYMP groups, while the complexity of both HP and QT variability at long time scales was under sympathetic control, being smaller during NIGHT and BBon in SYMP subjects. Complexity indexes at long time scales in ASYMP individuals were smaller than those in SYMP ones regardless of therapy (i.e. BBoff or BBon), thus suggesting that a reduced complexity of the sympathetic regulation is protective in ASYMP individuals. RMSE analysis of HP and QT interval variability derived from routine 24-hour electrocardiographic Holter recordings might provide additional insights into the physiology of the cardiac control and might be fruitfully exploited to improve risk stratification in LQT1 population.

Compression entropy contributes to risk stratification in patients with cardiomyopathy Kompressionsentropie zur verbesserten Risikostratifizierung bei Patienten mit DCM

Abstract Sudden cardiac death (SCD) is a leading cause of mortality with an incidence of 3 million cases per year worldwide. Therapies for patients who have survived an SCD episode or have a high risk of developing lethal ventricular arrhythmia are well established and depend mainly on risk stratification. In this study we investigated the suitability of the non-linear measure compression entropy (H C) for improved risk prediction in cardiac patients. We recorded 24-h Holter ECG for 300 patients with congestive heart failure (CHF). During a mean follow-up period of 12 months, 32 patients died due to a cardiac event. H C depends on the compression parameters window length w and buffer length b, which were optimised by analysing a subgroup of patients. Compression entropies based on the beat-to-beat interval (BBI) were subsequently calculated and compared with standard heart-rate variability parameters. Statistical analysis revealed significant differences between high- and low-risk CHF patients in standard HRV measures, as well as compression entropy based on the BBI (cardiac death, p=0.005; SCD, p=0.02). In conclusion, the implementation of non-linear compression entropy analysis in multivariate analysis seems to be useful for enhanced risk stratification of cardiac death, especially SCD, in ischaemic cardiomyopathy patients. Der plötzliche Herztod (SCD) ist die Haupttodesursache weltweit (3 Millionen Fälle/Jahr). Moderne Methoden zur Therapie und Prävention des SCD sind abhängig von der Erkennung der Hochrisikopatienten. Das Ziel dieser Studie war die Untersuchung der Eignung des nichtlinearen Parameters der Kompressionsentropie (H C) zur Risikostratifizierung bei ischämischer Herzinsuffizienz (CHF). Von 300 CHF-Patienten wurden 24-h Holter-EKGs im Rahmen einer spanischen Multicenter-Studie (MUSIC) aufgezeichnet. Innerhalb der anschließenden Follow-up-Phase (12 Monate) verstarben 32 Patienten aufgrund eines kardialen Ereignisses (Hochrisikogruppe). Mittels einer Patientenuntergruppe wurden die in die H C-Analyse eingehenden Parameter Fenster- und Bufferlänge optimiert. Zusätzlich zu der Berechnung von H C wurden die Standardparameter der Herzfrequenzvariabilität (HRV) bestimmt. Die statistische Analyse zeigte signifikante Unterschiede zwischen CHF-Patienten mit hohem und niedrigem Risiko in den Standardparametern der HRV (kardialer Tod: p=0,02; SCD: p=0,04) sowie Parametern der H C (kardialer Tod: p=0,005; SCD: p=0,02). Diese Ergebnisse zeigen die prinzipielle Eignung der H C für die Risikoanalyse des kardialen Todes insbesondere des plötzlichen Herztodes bei Patienten mit ischämischer Kardiomyopathie. Durch eine anschließende multivariate Analyse dieses nichtlinearen Parameters soll die Verbesserung der Ergebnisse bezüglich Sensitivität und Spezifität bestätigt werden.