Characterization of fibrillatory rhythms by ensemble vector directional analysis.

نویسندگان

  • Alan Kadish
  • David Johnson
  • Willie Choe
  • Jeffrey Goldberger
  • George Horvath
چکیده

Recent studies have demonstrated that fibrillatory rhythms are not random phenomena but have definable patterns. However, standard mapping techniques may have limitations in their ability to identify the organization of fibrillation. The purpose of this study was to develop and apply a method, "ensemble vector mapping," for characterizing the spatiotemporal organization of fibrillation. Ventricular fibrillation was induced by burst pacing in normal mongrel dogs. In a separate protocol, atrial fibrillation was induced by epicardial aconitine application. Epicardial electrograms were recorded from a 112-electrode plaque array using a computerized mapping system. Vectors were created by summing orthogonal bipolar electrograms. The magnitude of the vectors was transformed using a logarithmic function, integrated over time, and normalized for local electrogram amplitude to produce an "ensemble vector" index whose magnitude is high when beat-to-beat activation direction is consistent and low when activation direction is variable. The mean index was 137 +/- 36 mV/s during ventricular pacing at a cycle length of 300 ms but only 39 +/- 23 mV/s during ventricular fibrillation (P < 0.001). The ensemble vector index was also lower during atrial fibrillation (60 +/- 54 mV/s) than during atrial pacing (115 +/- 27 mV/s, P < 0.01 vs. atrial fibrillation) but not as low as during ventricular fibrillation (P < 0.05, atrial vs. ventricular fibrillation). The index was also capable of distinguishing atrial tachycardia from atrial fibrillation. Ensemble vector mapping produces an objective assessment of the consistency of myocardial activation during fibrillation. The consistency of activation direction differs in different models of fibrillation and is higher during atrial than ventricular fibrillation. This technique has the potential to rapidly characterize repetitive activation patterns in fibrillatory rhythms and may help distinguish among different characteristics of fibrillatory rhythms.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The coherence spectrum. A quantitative discriminator of fibrillatory and nonfibrillatory cardiac rhythms.

Previous work has suggested that a comparison of electrograms from two or more sites may best differentiate fibrillatory from nonfibrillatory rhythms. The coherence spectrum is a measure by which two signals may be compared quantitatively in the frequency domain. In the present study, the coherence spectrum was used to quantify the relation between spectral components of electrograms from two s...

متن کامل

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

The use of artificial intelligence in the process of diagnosing heart disease has been considered by researchers for many years. In this paper, an efficient method for selecting appropriate features extracted from electrocardiogram (ECG) signals, based on a genetic algorithm for use in an ensemble multi-kernel support vector machine classifiers, each of which is based on an optimized genetic al...

متن کامل

Application of ensemble learning techniques to model the atmospheric concentration of SO2

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

متن کامل

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

متن کامل

A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • American journal of physiology. Heart and circulatory physiology

دوره 285 4  شماره 

صفحات  -

تاریخ انتشار 2003