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 presen ted, Wavelet network, one nonlinear black-box modeling method, is used to i dentify 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 c onditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (N SRI), and ventricular fibrillation (VF), For NSR and NSRI, the proposed mod eling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF, This r esult suggests that a time-invariant nonlinear modeling method such as wave let network is not appropriate for VF rhythm, which is thought to be time-v arying as well as chaotic, but still useful in detection of VF, A new arrhy thmia detection algorithm, with potential application in implantable device s, is proposed for identifying the time of rhythmic bifurcation.