Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms.
A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.
Zhang
X S
XS
Department of Biomedical Engineering, College of Life Science and Biotechnology, Shanghai, China. zhangx5@rpi.edu
Zhu
Y S
YS
Thakor
N V
NV
Wang
Z M
ZM
Wang
Z Z
ZZ
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D004562:Electrocardiography / Q000379:methods*; D066298:In Vitro Techniques; D016014:Linear Models; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010496:Pericardium / Q000502:physiology*; D011817:Rabbits; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000503:physiopathology