Automatic Cycle Averaging for Denoising Approximately Periodic Spatiotemporal Signals

Optical mapping has become a common tool for cardiovascular research, providing high resolution spatiotemporal data of cardiac action potential propagation. However, noise in cardiac optical mapping (COM) data hampers quantitative measurements and analyses. Spatial and temporal filters have been used to increase the signal-to-noise ratio (SNR) of COM data. Although these help reduce noise and increase SNR, they also lower the spatial and temporal resolution of the data, which is not desirable. This paper utilizes the approximate periodicity of COM data to perform denoising by averaging cycles. We consider the entire approximately periodic spatiotemporal (APST) COM data as a concatenation of random samples generated from a deterministic single cycle spatiotemporal signal. The image difference signal (IDS) was defined and calculated to provide “global” periodicity information. Parameters for cycle segmentation, scaling and alignment were estimated based on the IDS. Finally, these parameters were used to segment, align and average cycles from each individual signal, which produces a clean and denoised single cycle spatiotemporal signal. The novel, fully automated pipeline was validated both qualitatively and quantitatively on zebrafish heart optical mapping data.