Wavelet analysis of cardiac optical mapping data.

Area of Science:

• Cardiac electrophysiology
• Biomedical signal processing
• Optical mapping technology

Background:

• Optical mapping is crucial for studying cardiac electrophysiology, but signals require preprocessing to enhance signal-to-noise ratio.
• Conventional Fourier analysis struggles with non-stationary signals like action potentials and can distort waveforms.
• Wavelet analysis offers simultaneous time-frequency localization, ideal for analyzing and reconstructing complex cardiac signals.

Purpose of the Study:

• To evaluate the efficacy of wavelet analysis for processing optical mapping signals.
• To compare wavelet-based methods against traditional filtering techniques like Fast Fourier Transformation (FFT) and Gaussian filters.
• To assess the impact of wavelet analysis on signal-to-noise ratio, waveform distortion, and accuracy of electrophysiological parameter estimation.

Main Methods:

• Discrete wavelet transformation was applied for temporal processing of optical mapping signals.
• Wavelet packet analysis was used for processing activation maps from simulated and experimental data.
• Wavelet methods were compared against FFT filtering (finite and infinite response) and Gaussian filtering.

Main Results:

• Wavelet analysis demonstrated superior signal-to-noise ratio improvement (5-10dB) and reduced action potential waveform distortion compared to FFT filtering.
• Spatial wavelet filtering yielded more effective denoising and accurate conduction velocity estimates than Gaussian filtering.
• Wavelet filtering provided superior revelation of cardiac propagation patterns.

Conclusions:

• Wavelet analysis is a powerful and promising tool for optical mapping signal processing.
• It facilitates accurate characterization of action potentials and formation of activation maps.
• Wavelet analysis improves the estimation of conduction velocity in cardiac tissue.