Background: Mapping technologies are powerful techniques for spatially recording electrophysiological behaviour of cardiac tissue, exploited by both clinicians and basic researchers. Processing and analysis of mapping data is difficult to implement, preventing uptake of these technologies and reliance on proprietary, ‘black-box’ software. Hence, we have developed open-source software for high-throughput analysis from mapping datasets, ElectroMap (available at github.com/CXO531/ElectroMap). Within ElectroMap, we have incorporated several developments including comprehensive conduction velocity and alternans modules, automatic pacing frequency detection and dominant frequency, phase mapping and wave similarity mapping for quantification of arrhythmic data. Here, we demonstrate the use of ElectroMap for identification of pro-arrhythmic phenomena in ex vivo optical and in vivo electrogram datasets.
Methods and results: ElectroMap was developed using MATLAB. Ex vivo hearts were optically mapped using transmembrane potentiometric (di-4/di-8-ANEPPS) and intracellular calcium (rhod-2AM) dyes. Virtual human endocardial virtual electrograms were collected
in vivo during ablation surgery using a non-contact multi-electrode array catheter (Ensite Array, St. Jude Medical, USA).
In murine left atria, increased pacing frequency (3 Hz to 10 Hz) and exposure to hypoxia are shown to significantly slow conduction velocity from
54.85 ± 4.45 cm/s to 48.71 ± 4.75 cm/s and 39.33 ± 4.14 cm/s respectively. Wave similarity analysis shows how sympathetic nervous stimulation (SNS) of guinea pig ventricles, which is preventive of ventricular fibrillation, also increases temporal regularity of the action potential (0.78 ± 0.02 versus 0.95 ± 0.008, control versus SNS, p=0.0011).
In vivo, in human right atrium during atrial fibrillation, higher dominant frequency components, marked phase discontinuities and greater wave dissimilarity are present compared with sinus rhythm. Importantly, a site of high frequency, possibly driving triggered activity, is identified during fibrillation.
Conclusions: ElectroMap allows quantifiable analysis of proarrhythmic phenomena from diverse mapping datasets. We anticipate that wider access to arrythmia analysis afforded by ElectroMap will support future research and broaden the use of mapping technologies.
