A study of human colonic motility in healthy and constipated subjects using the wireless capsule
Constipation is a common and distressing condition that has been linked to major morbidity, burdens the health care system, and impacts patients? quality of life. However, there is no perfect method for diagnosing and treating constipation. The purpose of this paper is to develop an automatic algorithm to identify patients with constipation from healthy subjects. Data from 12 healthy subjects and 10 patients with constipation were analyzed. The key challenges for data processing were data filtering, feature extraction, information evaluation, and providing the reference conclusion; these were resolved by employing the phase space reconstruction (PSR), independent component analysis (ICA), dynamic feature extraction algorithm, and the Wilcoxon rank sum test. The contractile frequency (Fr), motility index per unit time (MIU), average peak of peristaltic wave (Pave) and variance (Var) were extracted as dynamic parameters and analyzed. Results between groups were compared with the Wilcoxon rank sum test. There were statistically significant differences between healthy subjects and patients with constipation for Fr and MIU (P<0.05), whereas there was no statistically difference for Var. Moreover, the Fr and MIU of patients with normal transit constipation (NTC) are significantly lower compared to healthy subjects, whereas patients with slow transit constipation (STC) did not show significant differences. The proposed algorithms were able to differentiate between healthy subjects and patients with constipation based on the colonic motility profiles.
wireless capsule phase space reconstruction independent component analysis normal transit constipation slow transit constipation