Maximum likelihood cardiac conduction velocity estimation from sequential mapping in the presence of activation time noise with unknown variances

Mohammad Hassan Shariat, Saeed Gazor, Damian Redfearn
Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2016, 2016: 2745-2748
The cardiac conduction velocity (CV) can be estimated by analysing the activation times (ATs) and the locations of the electrodes that are used for the intracardiac electrogram (IEGM) recording. Here, we study the problem of the CV estimation in sequential mapping without using any independent electrogram as a time alignment reference. We assume that the IEGMs are sequentially recorded from several sites, where at each site, at least two of the catheter's electrodes are in contact with the cardiac tissue. We consider the planar wavefront with stable CV that propagates within the recording sites throughout our data collection period. Assuming the zero-mean Gaussian AT estimation error, we derive the maximum likelihood estimations of the CV and AT at a desired location on the cardiac shell. The CV is estimated when the variance of the AT estimation error and the time delay between the sequential recordings are unknown variables. Our simulation results show that the proposed method can precisely estimate the CV of the planar wavefront.


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