Automated classification of spatiotemporal characteristics of gastric slow wave propagation

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.