Wavelet analysis of cardiac optical mapping data

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 5-10dB SNR, and caused less distortion of the action potential waveform over the full range of simulated noise (5-20dB). 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.

[1]  Qionghai Dai,et al.  Joint Non-Gaussian Denoising and Superresolving of Raw High Frame Rate Videos , 2014, IEEE Transactions on Image Processing.

[2]  Edward Jones,et al.  An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression , 2013, Comput. Biol. Medicine.

[3]  Paul S. Addison,et al.  Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients , 2007, Comput. Biol. Medicine.

[4]  S. Knisley,et al.  Comparison of optical and electrical mapping of fibrillation , 2007, Physiological measurement.

[5]  G Indebetouw,et al.  Two-dimensional textural edge detection using -Gaussian filter. , 1986, Applied optics.

[6]  J. Rafiee,et al.  Wavelet basis functions in biomedical signal processing , 2011, Expert Syst. Appl..

[7]  Jean-Michel Poggi,et al.  Micronde: a Matlab Wavelet Toolbox for Signals and Images , 1995 .

[8]  Peter E Hammer,et al.  Optical mapping of Langendorff-perfused rat hearts. , 2009, Journal of visualized experiments : JoVE.

[9]  C. Chui,et al.  Wavelets : theory, algorithms, and applications , 1994 .

[10]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[11]  Nikolaos Arikidis,et al.  Interscale wavelet maximum - a fine to coarse algorithm for wavelet analysis of the EMG interference pattern , 2002, IEEE Transactions on Biomedical Engineering.

[12]  S Nattel,et al.  Functional mechanisms underlying tachycardia-induced sustained atrial fibrillation in a chronic dog model. , 1997, Circulation.

[13]  Rolf Unbehauen,et al.  An adaptive recursive 2-D filter for removal of Gaussian noise in images , 1992, IEEE Trans. Image Process..

[14]  Paul Scheunders,et al.  Wavelet Denoising of Multicomponent Images Using Gaussian Scale Mixture Models and a Noise-Free Image as Priors , 2007, IEEE Transactions on Image Processing.

[15]  Metin Akay,et al.  Time frequency and wavelets in biomedical signal processing , 1998 .

[16]  Adnan Bibic,et al.  Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[17]  William L. Ditto,et al.  A method for visualization of ventricular fibrillation: Design of a cooled fiberoptically coupled image intensified CCD data acquisition system incorporating wavelet shrinkage based adaptive filtering. , 1998, Chaos.

[18]  R. Bracewell.  The Fourier Transform and Its Applications , 1966 .

[19]  George S. Moschytz,et al.  A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients , 2003, IEEE Transactions on Biomedical Engineering.

[20]  Stanley Nattel,et al.  Model-Dependent Effects of the Gap Junction Conduction–Enhancing Antiarrhythmic Peptide Rotigaptide (ZP123) on Experimental Atrial Fibrillation in Dogs , 2007, Circulation.

[21]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[22]  F Zou,et al.  ECG data compression with wavelet and discrete cosine transforms. , 1994, Biomedical sciences instrumentation.

[23]  M. Palaniswami,et al.  Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals , 2012, Journal of Clinical Monitoring and Computing.

[24]  D. Sundararajan.  The Discrete Fourier Transform: Theory, Algorithms and Applications , 2001 .

[25]  Frederick J Vetter,et al.  Fluorescence imaging of cardiac propagation: spectral properties and filtering of optical action potentials. , 2006, American journal of physiology. Heart and circulatory physiology.

[26]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Jos B. T. M. Roerdink,et al.  Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing , 2004, IEEE Transactions on Medical Imaging.

[28]  Gary Hasey,et al.  Application of wavelet based denoising techniques to rTMS evoked potentials , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Jean-Claude Tardif,et al.  Atrial fibrillation promotion with long-term repetitive obstructive sleep apnea in a rat model. , 2014, Journal of the American College of Cardiology.

[30]  Reza Nezafat,et al.  Wavelet-Domain Medical Image Denoising Using Bivariate Laplacian Mixture Model , 2009, IEEE Transactions on Biomedical Engineering.

[31]  Keng Peng Tee,et al.  EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm , 2013, IEEE Transactions on Biomedical Engineering.

[32]  H. Lilly Beaulah,et al.  A Wavelet Transform Based Feature Extraction and Classification of Cardiac Disorder , 2014, Journal of Medical Systems.

[33]  Anuja Nair,et al.  Choosing the optimal mother wavelet for decomposition of radio-frequency intravascular ultrasound data for characterization of atherosclerotic plaque lesions , 2005, SPIE Medical Imaging.

[34]  Chrysostomos D. Stylios,et al.  Wavelet analysis for detection of phasic electromyographic activity in sleep: Influence of mother wavelet and dimensionality reduction , 2014, Comput. Biol. Medicine.

[35]  John L. Semmlow,et al.  Biosignal and Medical Image Processing , 2004 .

[36]  Wen-Liang Hwang,et al.  Wavelet Bayesian Network Image Denoising , 2013, IEEE Transactions on Image Processing.

[37]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[38]  Milos Doroslovacki,et al.  Preprocessing of fluoresced transmembrane potential signals for cardiac optical mapping , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Ahmad Reza Naghsh-Nilchi,et al.  Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function , 2012, IEEE Transactions on Image Processing.

[40]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[41]  Milos Doroslovacki,et al.  Signal Decomposition of Transmembrane Voltage-Sensitive Dye Fluorescence Using a Multiresolution Wavelet Analysis , 2011, IEEE Transactions on Biomedical Engineering.

[42]  Lawrence F. Shampine,et al.  The MATLAB ODE Suite , 1997, SIAM J. Sci. Comput..

[43]  J. N. Watson,et al.  Evaluating arrhythmias in ECG signals using wavelet transforms , 2000, IEEE Engineering in Medicine and Biology Magazine.

[44]  Adhemar Bultheel,et al.  Generalized cross validation for wavelet thresholding , 1997, Signal Process..

[45]  Yanjun Zeng,et al.  Research of fetal ECG extraction using wavelet analysis and adaptive filtering , 2013, Comput. Biol. Medicine.

[46]  R. Ideker,et al.  Estimation of conduction velocity vector fields from epicardial mapping data , 1998, IEEE Transactions on Biomedical Engineering.

[47]  Malcolm Atkinson,et al.  Computed tomography perfusion imaging denoising using Gaussian process regression , 2012, Physics in medicine and biology.

[48]  Ming-Hu Ha,et al.  Applications of wavelet transform in medical image processing , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[49]  I. Salama,et al.  Comparison of orthonormal adaptive FIR and IIR filter realizations , 1998, 1998 IEEE Symposium on Advances in Digital Filtering and Signal Processing. Symposium Proceedings (Cat. No.98EX185).

[50]  A. G. Ramakrishnan,et al.  ECG coding by wavelet-based linear prediction , 1997, IEEE Transactions on Biomedical Engineering.

[51]  G.A. Dumont,et al.  A wavelet approach to detecting electrocautery noise in the ECG , 2006, IEEE Engineering in Medicine and Biology Magazine.

[52]  M. N. Giri Prasad,et al.  A novel approach of image fusion on MR and CT images using wavelet transforms , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[53]  Stphane Mallat,et al.  A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way , 2008 .

[54]  Wei-Yen Hsu,et al.  Motor Imagery EEG Discrimination Using the Correlation of Wavelet Features , 2015, Clinical EEG and neuroscience.

[55]  Paul S Addison,et al.  Wavelet transforms and the ECG: a review , 2005, Physiological measurement.

[56]  Lars Kai Hansen,et al.  Model Selection for Gaussian Kernel PCA Denoising , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[57]  D C Reddy,et al.  Biorthogonal wavelet transforms for ECG parameters estimation. , 1999, Medical engineering & physics.

[58]  Wei-Ping Zhu,et al.  Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[59]  Margarita R. Gamarra Acosta,et al.  An innovative image-processing model for rust detection using Perlin Noise to simulate oxide textures , 2014 .

[60]  Brani Vidakovic,et al.  Image Denoising With 2D Scale-Mixing Complex Wavelet Transforms , 2014, IEEE Transactions on Image Processing.

[61]  D. Rosenbaum,et al.  Unique Properties of Cardiac Action Potentials Recorded with Voltage‐Sensitive Dyes , 1996, Journal of cardiovascular electrophysiology.

[62]  Shu Zhang,et al.  Estimation of 3-D conduction velocity vector fields from cardiac mapping data , 2000, IEEE Transactions on Biomedical Engineering.

[63]  Fu Siong Ng,et al.  Processing and analysis of cardiac optical mapping data obtained with potentiometric dyes. , 2012, American journal of physiology. Heart and circulatory physiology.

[64]  S Nattel,et al.  Mathematical analysis of canine atrial action potentials: rate, regional factors, and electrical remodeling. , 2000, American journal of physiology. Heart and circulatory physiology.

[65]  Qiuting Huang.  Mixed analog/digital, FIR/IIR realization of a linear-phase lowpass filter , 1996 .