An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data.

Area of Science:

• Cardiac electrophysiology
• Biomedical optics
• Signal processing

Background:

• Optical mapping is crucial for studying cardiac electrophysiology in ex vivo models.
• Processing fluorescence-optical data from thin mammalian myocardium presents challenges due to low signal-to-noise ratio (SNR).
• Effective digital filtering is essential for accurate analysis of optical mapping data.

Purpose of the Study:

• To introduce and evaluate an adaptive spatio-temporal Gaussian filter for processing optical mapping signals.
• To demonstrate automatic selection of filter parameters without user intervention.
• To compare the proposed filter against standard methods for low SNR data.

Main Methods:

• Development of an adaptive spatio-temporal Gaussian filter.
• Generation of synthetic optical mapping signals with varying SNR from rat atrial myocardium.
• Application and comparison of the filter with existing methods on both synthetic and experimental ex vivo data.

Main Results:

• The adaptive filter outperformed standard methods in detecting local activation times at SNRs below 3 dB.
• Performance was comparable or slightly less effective than literature methods at higher SNRs.
• Automatic parameter adaptation was achieved, unlike in other investigated filters.

Conclusions:

• The proposed adaptive spatio-temporal Gaussian filter is effective for analyzing fluorescence-optical data with low SNR.
• This method offers an automated approach to filtering, simplifying data analysis in cardiac electrophysiology.
• The filter shows particular promise for investigating signals from thin mammalian myocardial tissues.