Background Optical mapping is a powerful research tool that is revolutionising study of cardiac electrophysiology. However, processing and analysis of optical mapping data is computationally challenging to design and implement, a difficulty further enhanced by novel camera technology providing higher temporal and spatial resolution. We have previously developed algorithms capable of objective processing and analysis of electrophysiological parameters acquired using a second-generation complementary metal-oxide semiconductor camera.¹ Here we report development of improved algorithms packaged in a user-friendly graphical interface, capable of high-throughput processing and analysis of voltage and calcium optical mapping data from a wide spectrum of cameras. Functionality and processing speed is further improved through automated recognition of pacing frequency and analysis of activation and repolarisation of electrophysiological parameters at high spatiotemporal resolution (200 × 2400 pixels; sampling rate 1 kHz). Processing options allow averaging of multiple beats as well as individual beat segmentation within the whole experimental trace, thus allowing for study of dynamic changes in key parameters such as action potential duration (APD), activation times, conduction velocity (CV) and phase mapping.

Methods and results We have compared how our software performs in terms of analysis and processing time with our previously published methods.¹Results remain consistent between both methods (APD50=10.41±0.62 vs 10.36±0.63 ms; CV=24.3±1.68 vs 23.3±1.69 cm/s, new software vs published methods; n=5, murine atria). Substantial improvements in processing speed (up to 4 times) are achieved, compared to our previously published methods. This improvement, coupled with automatic recognition and segmentation of whole experiments by cycle length, enables analysis of frequency dependent beat-to-beat changes in APD, CV and activation times. We demonstrate dynamic beat-to-beat frequency-dependent changes in APD50 in isolated superfused murine atria over a long experimental timeframe. As expected, increased frequency reduces APD50 (3 Hz=12.56±0.08; 8.33Hz=11.78±0.29; 10 Hz=11.47±0.3; 12.5Hz=10.88±0.29 ms; n=3), however, extensive beat-to-beat analysis allows investigation of electophysiological changes over whole experimental protocols, potentially providing crucial insight into novel arrhythmic mechanisms.

Conclusions We have developed high-throughput software for analysis and processing of optical mapping imaging data compatible with a wide range of optical mapping cameras/systems. Our software offers enhanced processing speeds of key electrophysiological parameters across the heart and allows beat-to-beat analysis of large computationally challenging datasets.

Reference 1. Yue et al. Prog Biophys Mol Biol. 2014 Aug;115(2-3):340–8.

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Abstract 188 Figure 1

Graphical user interface offering parameter options for image processing, filtering and statistics. Software allows signal segmentation into single or multiple beats for analysis. Inset: Beat-to-beat analysis of frequency-dependent changes in action potential duration (APD50) in paced isolated murine atria.