Colonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias.
The main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis.
cHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg).
The automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours.
An automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent.