Wavelet analysis of cardiac optical mapping data. (1st October 2015)

• Record Type: Journal Article
• Authors: Xiong, Feng; Qi, Xiaoyan; Nattel, Stanley; Comtois, Philippe
• Is Part Of: Computers in biology and medicine. Volume 65(2015)
• Page Start: 243
• Page End: 255
• Publication Date: 2015-10-01
• Subjects: Signal processing -- Action potential -- Optical mapping -- Filtering methods -- Cardiac electrophysiology -- Conduction analysis -- Bioengineering -- Heart arrhythmias; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.06.022
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Optical mapping technology is an important tool to study cardiac electrophysiology. Transmembrane fluorescence signals from voltage-dependent dyes need to be preprocessed before analysis to improve the signal-to-noise ratio. Fourier analysis, based on spectral properties of stationary signals, cannot directly provide information on the spectrum changes with respect to time. Fourier filtering has the disadvantage of causing degradation of abrupt waveform changes such as those in action potential signals. Wavelet analysis has the ability to offer simultaneous localization in time and frequency domains, suitable for the analysis and reconstruction of irregular, non-stationary signals like the fast action-potential upstroke, and better than conventional filters for denoising. Methods: We applied discrete wavelet transformation for temporal processing of optical mapping signals and wavelet packet analysis approaches to process activation maps from simulated and experimental optical mapping data from canine right atrium. We compared the results obtained with the wavelet approach to a variety of other methods (Fast Fourier Transformation (FFT) with finite or infinite response filtering, and Gaussian filters). Results: Temporal wavelet analysis improved signal-to-noise ratio ( SNR ) better than FFT filtering for 5–10 dB SNR, and caused less distortion of the action potential waveform over the full range of simulated noise (5–20 dB). Spatial wavelet filtering produced more efficient denoising and/or more accurate conduction velocity estimates than Gaussian filtering. Propagation patterns were also best revealed by wavelet filtering. Conclusions: Wavelet analysis is a promising tool, facilitating accurate action potential characterization, activation map formation, and conduction velocity estimation. Highlights: Optical mapping (OM) technology is important to study cardiac electrophysiology. Wavelet analysis offers simultaneous localization in time and frequency domains. Wavelet analysis improved the signal quality compared to Fourier-based filtering. Spatial wavelet filtering produced more accurate conduction velocity estimates. Wavelet analysis is a promising tool for OM electrophysiological characterization.