Study On The Temporal And Spatial Analysis And Measurement Of Body Surface Potential

The cardiac activation spread is a spatial and temporal function. Non invasively obtained temporal and spatial information may be important in cardiac electrophysiology exploration. The body surface potential mapping (BSPM), as one of the advanced recording technique, played a significant role in the heart disease diagnosis. Solving the inverse problem is made that the body surface potentials are used to described the cardiac electrical activity. The inverse problem does not posses a unique solution. This difficulty is mitigated by properly selecting the equivalent sources and regularization. The recently developed body surface Laplacian mapping (BSLM) technique was found to provide better spatial resolution for localizing and imaging cardiac activity than BSPM. So it's great attention that surface Laplacian be approximately calculated from surface potential or directly measured on the body.This paper mainly discussed the problems about non-invasively mapping cardiac electrical activity as follows.Based on the topological foundations of electrocardiology, the singularities of the first time derivative of body surface potential was detected by using wavelet transform. The singularity detect results are corresponding to the critical times of ventricular depolarization by estimating local Lipschitz exponents. The method that localized critical points with Maximal Correlation was presented. By simulating the propagating of ventricular depolarization, body surface potentials were calculated. Critical points were localized for simulating potentials and noisy potentials.The relativities of the temporal principal component of multichannel surface potentials and the cumulated energy percentage were analyzed by singular value decomposition (SVD). Spatial principal component analysis confirmed that result. By using the former researchers'study of the redundancy of the body surface mapping electrodes for reference, an optimal electrode selection method that based on multiple variable linear regression was presented. The performance of the algorithm was evaluated using spatial Root Mean Square (RMS). By combined serial forward search (SFS) and serial backward search (SBS), called Zl-Zr search, the optimal electrode sites were selected and the site distributing role was summarized.The analytical expression of the potential generated by arbitrarily located in the two layer concentric sphere was rededuced, and the body surface Laplacian were approximately calculated from potential by spline interpolation. The relative error of spline interpolation to analytical potential is calculated for different electrode numbers and locations. A tripolar concentric ring electrode sensor based on the nine-point difference was designed for surface Laplacian sensing. We found that the sensor has a much-improved accuracy with bipolar sensor in estimating the Laplacian operator. The experimental results are in agreement with the theoretical calculations suggesting the feasibility of measuring the surface Laplacian. We picked up the Laplacian ECG from healthy human body surface by the active tripolar sensor. A non-linear adaptive filter and walvelet denoising technique were used to eliminate power line and other interferences, and got the high quality Laplacian ECG signal. It lays the foundation for using such Laplacian ECG to assist in heart disease diagnosis.