Abstract
In this study, an automated algorithm was developed to identify the arrhythmic gastric slow wave activity that was recorded using high-resolution mapping technique. The raw signals were processed with a Savitzky-Golay filter, and the slow wave activation times were identified using a threshold-varying method and grouped using a region-growing method. Slow wave amplitudes and velocities were calculated for all cycles. Arrhythmic events were identified when the orientation of a slow wave at an electrode exceeded the 95% confidence interval of the averaged orientation of several normal cycles. A second selection criterion was further developed to identify the arrhythmic events by an anisotropy ratio. In both pig and human studies, arrhythmias were associated with the emergence of circumferential velocity components and higher amplitudes.