Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA.
IEEE/ACM Trans Comput Biol Bioinform. 2013 Mar-Apr;10(2):323-36. doi: 10.1109/TCBB.2012.125.
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 weighted overlapping of adjacent segments which increases the smoothness at join points. SCA has been applied to a number of representative types of spiral waves, and, for each type, a distinct curvature evolution in time (signature) has been identified. Distinct signatures have also been identified for spiral breakup. These results represent a significant first step in automatically determining parameter ranges for which a computational cardiac-cell network accurately reproduces a particular kind of cardiac arrhythmia, such as ventricular fibrillation.
我们提出了螺旋分类算法（SCA），这是一种用于分类心脏组织中电螺旋波及其相关碎裂的快速准确算法。SCA 执行的分类是检测和分析各种心律失常疾病（包括室性心动过速和颤动）的重要组成部分。给定传播波的数字化帧，SCA 构建波的前后的高度精确表示，用三次样条分段内插该表示，并对结果进行精确的曲率分析。这种分析比基于螺旋尖端跟踪的方法更全面，因为它考虑了整个波前和波后。为了增加符号表示的平滑度，SCA 使用相邻段的加权重叠，这增加了连接点处的平滑度。SCA 已经应用于几种有代表性的螺旋波类型，并且对于每种类型，都确定了时间上的独特曲率演化（特征）。螺旋碎裂也有独特的特征。这些结果代表了自动确定计算心脏细胞网络准确再现特定类型心律失常（如心室颤动）的参数范围的重要的第一步。
