Detection of Slow Wave Propagation Direction Using Bipolar High-Resolution Recordings

Gastric motility is in part coordinated by bio-electrical slow waves. The wavefront orientation of the slow wave contains vital physiological information about the motility condition of the gastrointestinal system. Dysmotility was shown to be associated with dysrhythmic propagation of the slow wave. The most commonly used method to detect wavefront orientation is computationally expensive because of the involvement of activation time identification. The information of local directionality contained in bipolar slow wave recordings could be used to detect the wavefront orientation. An algorithm called bipolar direction detection was developed to utilize the information contained in the bipolar slow wave recordings. Bipolar recordings were constructed by subtracting the unipolar in vivo recordings of directional electrode pairs. Then, time delay information was used to detect the wavefront direction. The algorithm was verified using synthetic data and validated using experimental data. Ten high-resolution in vivo slow wave signals from 5 pigs were recorded for a duration of 2 minutes. The performance was compared against the semi-automated approach, resulting in an average angle error of 20° for the experimental data. The algorithm was able to detect slow wave wavefront orientation with minimal errors rapidly.Clinical relevance-The ability to rapidly detect slow wave propagation direction will enable effective analysis of large data sets, through which we can obtain a better understanding of functional motility disorders and help with diagnosis and treatment.

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IV^TM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF.

Impact of bipolar electrodes contact on fractionation index measurement

Signals such as Complex Fractionated Atrial Electrograms (CFAE) are tracked during ablation procedures to locate the arrhythmical substrate regions. Most of CFAE classification tools use fractionation indexes. However, recordings from intracardiac catheter depend on electrode contact quality. This paper investigates the impact of electrode contact area on fractionation indexes. It is assessed through three kinds of arrhythmical activations resulting from a numerical simulation of a small piece of the cardiac tissue. Bipolar electrograms are extracted corresponding to 25 different contact areas and fractionation indexes (Shannon entropy, non linear energy operator and maximum peak ratio) are computed. Results yield that the Shannon entropy offers a good potential discrimination between arrhythmic scenarios and is less sensitive to the electrode contact variation.

Evaluation of the complexity of myocardial activation during ventricular fibrillation. An experimental study

INTRODUCTION AND OBJECTIVES

An experimental model is used to analyze the characteristics of ventricular fibrillation in situations of variable complexity, establishing relationships among the data produced by different methods for analyzing the arrhythmia.

METHODS

In 27 isolated rabbit heart preparations studied under the action of drugs (propranolol and KB-R7943) or physical procedures (stretching) that produce different degrees of change in the complexity of myocardial activation during ventricular fibrillation, use was made of spectral, morphological, and mapping techniques to process the recordings obtained with epicardial multielectrodes.

RESULTS

The complexity of ventricular fibrillation assessed by mapping techniques was related to the dominant frequency, normalized spectral energy, signal regularity index, and their corresponding coefficients of variation, as well as the area of the regions of interest identified on the basis of these parameters. In the multivariate analysis, we used as independent variables the area of the regions of interest related to the spectral energy and the coefficient of variation of the energy (complexity index=-0.005×area of the spectral energy regions -2.234×coefficient of variation of the energy+1.578; P=.0001; r=0.68).

CONCLUSIONS

The spectral and morphological indicators and, independently, those derived from the analysis of normalized energy regions of interest provide a reliable approach to the evaluation of the complexity of ventricular fibrillation as an alternative to complex mapping techniques.