An application of statistical pattern recognition technique to the classification of canine duodenal contractile activity resulting from the ingestion of three different test meals, viz. water, acid, and solid, is described. Feature vectors were extracted from the contractile activities recorded by means of strain gauges, and were based on average duration, number, and intensities of contractions, as well as their frequency and propagation characteristics. Pattern training and classification were performed via the Bayes decision rule. The results indicate that the three different meals give rise to three different kinds of contractile activities. An improvement in the classifier's performance was observed when it was updated with correctly classified patterns. The results appear to suggest that pattern analysis techniques add a new dimension to the study of gastrointestinal motility in health and disease. For example, pattern recognition techniques can also be extended to identify contractile patterns altered due to such pathological conditions as irritable bowel syndrome and postoperative diarrhea and, hence, elucidate the underlying relationship between contractile activity and distal propulsion of contents.
ASJC Scopus subject areas
- Biomedical Engineering