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High-Throughput Analysis of Optical Mapping Data Using ElectroMap
Published on: June 4, 2019
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Wavelet analysis of cardiac optical mapping data.
Feng Xiong1, Xiaoyan Qi2, Stanley Nattel3
1Department of Pharmacology and Therapeutics, McGill University, Montreal, Que., Canada; Research Center, Montreal Heart Institute and Université de Montréal, 5000 Belanger Street East, Montreal, Que., Canada H1T 1C8.
Computers in Biology and Medicine
|July 26, 2015
Summary
Wavelet analysis enhances optical mapping signals by improving signal-to-noise ratio and preserving action potential waveforms. This advanced technique offers superior denoising and more accurate cardiac electrophysiology analysis compared to traditional methods.
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.
