Dynamic analysis of ventricular repolarization duration from 24-hour Holter recordings.

Evaluation of the influence of the autonomic nervous system on the ventricular repolarization duration was carried out using beat-to-beat analysis of the time intervals between the peaks of the R and T waves (RTm). After pre-processing of digitized Holter ECG's, auto and cross spectrum analyses were applied to heart rate and repolarization duration variability signals. Coherence analysis was used to assess the existence of common spectral contributions. The heart rate variability signal was used as reference of the sympatho-vagal balance at the sinus node. It was found that, in normal individuals, the autonomic nervous system directly influences the ventricular repolarization duration and that this influence is qualitatively very similar to the one that modulates the heart rate. Pathological alteration of these parallel autonomic activities to the heart (on the sinus node and on the ventricle) might cause uncoupling between depolarization and repolarization.

Relation between ventricular repolarization duration and cardiac cycle length during 24-hour Holter recordings. Findings in normal patients and patients with long QT syndrome.

BACKGROUND: The interval from the R wave to the maximum amplitude of the T wave (RTm) contains the heart rate dependency of ventricular repolarization. METHODS AND RESULTS: A computer algorithm was developed to quantify the RTm and preceding RR intervals for each of more than 50,000 beats on 24-hour ambulatory electrocardiographic (Holter) recordings to evaluate the dynamic relation between repolarization duration and cycle length. The relation of RTm to the preceding RR interval (RTm/RR slope) was determined by the best-fit linear regression equation between these two parameters. Eleven normal subjects and 16 patients with long QT syndrome (LQTS) were investigated. Six of the normal subjects had Holter recordings obtained before and after beta-blocker therapy. beta-Blockers were associated with a significant (p = 0.005) reduction in the RTm/RR slope from 0.13 +/- 0.02 to 0.10 +/- 0.02. The mean value of the RTm/RR slope was significantly (p = 0.003) larger in the LQTS patients (0.21 +/- 0.08) than in normal subjects (0.14 +/- 0.03). CONCLUSIONS: These findings indicate that 1) quantification of the dynamic relation between ventricular repolarization and RR cycle length can be obtained on a large number of Holter-recorded heart beats; 2) beta-blockers reduce the RTm/RR slope in normal patients; and 3) LQTS patients have an exaggerated delay in repolarization at long RR cycle lengths.

Long QT syndrome. New electrocardiographic characteristics.

The long QT syndrome is electrocardiographically characterized by a prolonged QT interval and by several other, more subtle, ST-T-U wave abnormalities, most of which have not been quantified. To determine the possible usefulness of several new electrocardiographic characteristics in identifying patients with known long QT syndrome, logistic regression models were applied to a data base of seven new, relatively independent, electrocardiographic repolarization variables. These were measured on digitized 12-lead electrocardiograms of 315 normal subjects and 37 patients with the long QT syndrome (members of well-identified long QT syndrome families, QTc greater than 0.44 second, 27% symptomatic), who ranged in age from 17 to 60 years. Electrocardiographic variables that independently differentiated (p less than 0.001) patients with long QT syndrome from normal subjects included quantitative measures of repolarization: early duration, rate, T wave symmetry, late phenomena, and heterogeneity. All selected repolarization variables except the early duration variable were essentially independent of the QTc (r2 less than 0.15), and all contributed significantly to the identification of patients with long QT syndrome. A classification model of five electrocardiographic predictor variables resulted in an estimated sensitivity (95% confidence interval) of 92.6% (81.6-100%) and an estimated specificity (95% confidence interval) of 95.8% (93.6-98.1%). This model performed significantly better than an alternative classification model that was based on the early duration variable as a single predictor variable. The symptomatic status of patients with long QT syndrome could not be predicted by any combination of the electrocardiographic variables in the investigated model.

Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability.

The R-R interval measurement from digitized electrocardiograms (ECG) contains an error due to the finite sampling frequency which may jeopardize the beat-to-beat analysis of the heart rate. In this paper, we develop a model to describe and quantitate this error. The "measured" R-R interval is modeled as the sum of the "true" R-R interval and of the error of measurement. The first and second order statistics of the error are computed in order to investigate its influence on the heart rate variability (HRV) power spectrum. They are found to be only functions of the ECG sampling frequency and, in particular, the power spectrum of the error contributes an additive high-pass filter-like term (colored noise) to the power spectrum of the HRV. The accuracy of the model is tested via a simulation procedure. The model indicates that the relative balance between the HRV and the error power spectra is important and should be checked before any variability analysis on the heart rate. This balance may be favorable to the error when 1) the sampling frequency of the ECG is too low, and/or 2) the variability of the heart rate is too little. In these cases, the HRV spectrum analysis may not give reliable results. Two tests are proposed in order to evaluate the error influence either in specific frequency bands or in the total frequency range.

Preprocessing of Holter ECGs for analysis of the dynamic interrelations between heart rate and ventricular repolarization duration variabilities.

The use of approved commercial digital Holter systems as equipment for data acquisition in cardiologic research presents several potential advantages. In fact, it may provide a standard data acquisition procedure that allows the scientists to concentrate on the data processing phase. As a consequence, it may contribute to the comparability of results among different laboratories. In general, some steps of preprocessing are needed before performing the final analysis on the data. In case of study on the variabilities of the heart rate and the duration of repolarization, preprocessing consists of beat-by-beat measurement time intervals in the 24-hour electrocardiograms. Further preprocessing steps may be required, depending on the particular analysis technique that will be applied to the data. In particular, the present study introduces an operator-free method of preprocessing Holter electrocardiograms for time series analysis of signals related to heart rate variability and variability in the duration of ventricular repolarization.

Electrocardiographic quantitation of ventricular repolarization.

Quantification of the electrocardiographic ventricular repolarization involving the T-U wave complex is usually performed with reference to the axis of the T wave and the QT interval duration. A novel quantitative approach to improve the description of ventricular repolarization was applied to the digitized electrocardiograms of 423 normal subjects. Six electrocardiographic repolarization characteristics were identified: duration, rate, area, symmetry, late phenomena, and interlead heterogeneity. A computer algorithm was designed to automatically interpret the electrocardiographic repolarization segment and measure 11 variables that quantified these repolarization characteristics. The application of redundancy-reduction techniques selected a final set of seven variables that were used in the statistical analysis. The QT interval, which was included in the initial group of variables, was replaced by the time interval between S wave offset and T wave maximum. All selected electrocardiographic variables were independent of age (r2 less than 0.11) and body surface area (r2 less than 0.03); all except the early duration variable were heart rate- and QT interval-independent (r2 less than 0.2, r2 less than 0.13, respectively; and most were uncorrelated to each other. A comparison of repolarization characteristics by gender revealed that repolarization duration was significantly more prolonged (p less than 0.0001) in women than in men. This multidimensional quantitative approach conveys a new and more complete description of the repolarization process and provides an electrocardiographic repolarization database in normal subjects as a reference standard for identifying patients with disordered repolarization.

Automatic assessment of the interaction between respiration and heart rate variability signal.

The present paper introduces an original method of processing heart rate variability (HRV) and respiration signals as detected respectively through chest electrodes and thoracic belt in dogs under different experimental conditions. Signals are processed as time series synchronous with the occurrence of QRS complexes on ECG signal and auto and cross spectra are accordingly calculated. Two particular bands appear mainly of interest on the spectrum of HRV signal: one in correspondence with the respiration rate and another one at a lower frequency value. Values of power at these frequency bands together with coherence and phase between HRV signal and respiration complete the parameters which try to quantify a few aspects of the complex dynamic relationships between the original signals. In particular, controlled respiration in dogs was studied through the connection with an automatic ventilator, as well as the effects of drugs which interact with the neural regulatory systems (i.e. sympathetic and parasympathetic nervous system). Gain and phase relationships between heart rate variability and respiration, obtained with spectral analysis, could be used to provide a better understanding of the neural control mechanisms linking heart rate and respiration in various experimental conditions. The method described in this study is to be used both in physiological and clinical research.

Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies.

The heart rate variability (HRV) signal carries important information about the systems controlling heat rate and blood pressure, mainly elicited by autonomic nervous system (sympathetic and parasympathetic) controls. The present paper illustrates methods of HRV signal processing by using autoregressive (AR) modeling and power spectral density estimate. The information enhanced in this way seems to be particularly sensitive in discriminating various cardiovascular pathologies (hypertension, myocardial infarction, diabetic neuropathy, etc.). This method provides a simple non-invasive analysis, based on the processing of spontaneous oscillations in heart rate. Particular emphasis is directed to the algorithms used and to their direct application by using proper computerized techniques: only a few paradigmatical examples will be illustrated as preliminary results.