Automatic feature extraction algorithms for the assessment of in-vitro electrical recordings of rat myocardium with ablation lesions

Abstract

Abstract Cardiac arrhythmias are a widely spread disease in industrialized countries. A common clinical treatment for this disease is radiofrequency ablation (RFA), in which high frequency alternating current creates a lesion on the myocardium. However, the formation of the lesion is not entirely understood. To obtain more information about ablation lesions (ALs) and their electrophysiological properties, we established an in-vitro setup to record electrical activity of rat myocardium. Electrical activity is measured by a circular shaped multielectrode array. This work was focused to gain more information by developing algorithms to process the measured electrical signals to collect different features, which may allow us to characterize an AL. First, pacing artefacts were detected and blanked. Subsequently, data were filtered. Afterwards, activations in atrial signals were detected using a non-linear energy operator (NLEO) and templates of these activations were generated. Finally, we determined different features on each activation in order to evaluate changes of unipolar as well as bipolar electrograms and considered these features before and after ablation. In conclusion, the majority of the signal features delivered significant differences between normal tissue and lesion. Among others, a reduction in peak to peak amplitude and a diminished spectral power in the band 0 to 100 Hz may be useful indicators for AL. These criteria should be verified in future studies with the aim of estimating indirectly the formation of a lesion.