Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation

Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.

Modelling the heart as a communication system

Electrical communication between cardiomyocytes can be perturbed during arrhythmia, but these perturbations are not captured by conventional electrocardiographic metrics. We developed a theoretical framework to quantify electrical communication using information theory metrics in two-dimensional cell lattice models of cardiac excitation propagation. The time series generated by each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon entropy for each cell was calculated from the time series during four clinically important heart rhythms: normal heartbeat, anatomical reentry, spiral reentry and multiple reentry. We also used mutual information to perform spatial profiling of communication during these cardiac arrhythmias. We found that information sharing between cells was spatially heterogeneous. In addition, cardiac arrhythmia significantly impacted information sharing within the heart. Entropy localized the path of the drifting core of spiral reentry, which could be an optimal target of therapeutic ablation. We conclude that information theory metrics can quantitatively assess electrical communication among cardiomyocytes. The traditional concept of the heart as a functional syncytium sharing electrical information cannot predict altered entropy and information sharing during complex arrhythmia. Information theory metrics may find clinical application in the identification of rhythm-specific treatments which are currently unmet by traditional electrocardiographic techniques.

Effects of the location of myocardial infarction on the spectral characteristics of ventricular fibrillation

BACKGROUND

The location of the myocardial infarction (MI) might modify the spectral characteristics of ventricular fibrillation (VF) in humans.

OBJECTIVE

To evaluate the effect of the location of the infarcted area on the spectral parameters of VF.

METHODS

Patients with chronic MI (29 anterior, 32 inferior) and induced VF during cardioverter defibrillator implant were retrospectively studied. Dominant frequency (f(d)), organization index (OI), and power of the harmonic peaks were calculated in the device-stored electrograms (EGM) during sinus rhythm (SR) and VF.

RESULTS

The f(d) of the VF was not affected by the left ventricular ejection fraction (LVEF) or the MI location (anterior: 4.54 +/- 0.74 Hz, inferior: 4.77 +/- 0.48 Hz, n.s.). The OI was also similar in both groups. However, in patients with inferior MIs, normalized peak power at f(d) was higher (118.3 +/- 18.5 vs 100.6 +/- 28.2, P < 0.01) and the normalized peak power of the harmonics was lower than in the anterior MI group. The analysis of EGM during SR showed similar results. The size of the necrotic area and its distance to the recording electrode might partially explain these results.

CONCLUSION

In our series, the spectral characteristics of the EGMs during VF showed significant differences depending on the MI localization. A higher fraction of energy (in the low-frequency region) was seen in inferior MIs, whereas the peak power at the harmonics increased in anterior MIs. A similar effect was seen during SR and VF, suggesting that it is caused by local electrophysiology abnormalities induced by the MI rather than by different intrinsic characteristics of the VF.