Automated detection of gastric slow wave events and estimation of propagation velocity vector fields from serosal high-resolution mapping

High-resolution (HR; multi-electrode) recordings have led to detailed spatiotemporal descriptions of gastric slow wave activity. The large amount of data conveyed by the HR recordings demands an automated way of extracting the key measures such as activation times. In this study, a derivative-based method of identifying slow wave events was proposed. The raw signal was filtered using a second order Butterworth filter (low-pass; 10 Hz). The signal in each channel was differentiated and a threshold was taken as the 4.5x of the average of the negative first derivatives. An active event was defined where the first derivatives of the signal were more negative than the threshold. The accuracy of the method was validated against manually marked times, with a positive predictive value of 0.71. The detected activation times were interpolated using a second-order polynomial, the coefficients of which were evaluated using a previously developed least-square fitting method. The velocity fields were calculated, showing detailed spatiotemporal profile of slow wave propagation. The average of slow wave propagation velocity was 5.86 ± 0.07 mms ^-1.