Paskaranandavadivel Niranchan, Gao Jerry, Du Peng, O'Grady Gregory, Cheng Leo K
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:7342-5. doi: 10.1109/EMBC.2013.6611254.
Gastric contractions are underpinned by an electrical event called slow wave activity. High-resolution electrical mapping has recently been adapted to study gastric slow waves at a high spatiotemporal detail. As more slow wave data becomes available, it is becoming evident that the spatial organization of slow wave plays a key role in the initiation and maintenance of gastric dsyrhythmias in major gastric motility disorders. All of the existing slow wave signal processing techniques deal with the identification and partitioning of recorded wave events, but not the analysis of the slow wave spatial organization, which is currently performed visually. This manual analysis is time consuming and is prone to observer bias and error. We present an automated approach to classify spatial slow wave propagation patterns via the use of Pearson cross correlations. Slow wave propagations were grouped into classes based on their similarity to each other. The method was applied to high-resolution gastric slow wave recordings from four pigs. There were significant changes in the velocity of the gastric slow wave wavefront and the amplitude of the slow wave event when there was a change in direction to the slow wave wavefront during dsyrhythmias, which could be detected with the automated approach.
胃收缩由一种称为慢波活动的电活动支撑。高分辨率电标测最近已被用于在高时空细节上研究胃慢波。随着更多慢波数据的获取，越来越明显的是，慢波的空间组织在主要胃动力障碍中胃节律紊乱的起始和维持中起着关键作用。现有的所有慢波信号处理技术都用于处理记录的波事件的识别和划分，而不是慢波空间组织的分析，目前慢波空间组织的分析是通过视觉进行的。这种人工分析耗时且容易出现观察者偏差和误差。我们提出了一种通过使用皮尔逊互相关来对空间慢波传播模式进行分类的自动化方法。慢波传播根据它们彼此的相似性被分组。该方法应用于来自四只猪的高分辨率胃慢波记录。在节律紊乱期间，当慢波波前方向发生变化时，胃慢波波前速度和慢波事件幅度有显著变化，这可以通过自动化方法检测到。
