Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification - Rajkumar Palaniappan (School of Mechatronic Engineering, Universiti Malaysia Perlis, 2015)
This thesis discusses the development of a computerized decision support system (CDSS) to detect respiratory pathology using pulmonary acoustic signals. The pulmonary acoustics signals were collected from 72 subjects to develop the CDSS. In order to develop the CDSS tool, three different methodological frameworks were proposed to determine the most effective classification of respiratory pathology. The recorded pulmonary acoustics signals were filtered to remove noise and other artifacts followed by respiratory cycle segmentation.