We present the Spiral Classification Algorithm (SCA), a fast and accurate algorithm for classifying electrical spiral waves and their associated breakup in cardiac tissues. The classification performed by SCA is an essential component of the detection and analysis of various cardiac arrhythmic disorders, including ventricular tachycardia and fibrillation. Given a digitized frame of a propagating wave, SCA constructs a highly accurate representation of the front and the back of the wave, piecewise interpolates this representation with cubic splines, and subjects the result to an accurate curvature analysis. This analysis is more comprehensive than methods based on spiral-tip tracking, as it considers the entire wave front and back. To increase the smoothness of the resulting symbolic representation, the SCA uses a weighted overlapping of adjacent segments which increases the smoothness at join points. To significantly speed up SCA computation time, we develop a GPU-CUDA implementation of SCA. SCA has been applied to several representative types of spiral waves, and for each type, a distinct curvature evolution in time (signature) has been identified. Moreover, distinguished signatures have been also identified for spiral breakup. This represents a significant first step in automatically determining parameter ranges for which a computational cardiac-cell network accurately reproduces ventricular fibrillation. The connection between parameters and physiological entities would then lead to an understanding of the root cause of the disorder and enable the development of personalized treatment strategies.
In Proc. of CMSB'11, the 9th International Conference on Computational Methods in Systems Biology, Paris, France, September, 2011, ACM.
*This work was supported by the NSF Expeditions Award CNS-09-26190, the
NSF CSR-AES05-09230 Award and the AFOSR FA-0550-09-1-0481 Award.