TY - JOUR AU1 - Palaniappan, Rajkumar AU2 - Sundaraj, Kenneth AU3 - Sundaraj, Sebastian AU4 - Huliraj, N. AU5 - Revadi, S.S. AB - fuzzy inference system – pulmonary acoustic Background: Monitoring respiration is important in several medical applications. signal – respiratory phase detection – One such application is respiratory rate monitoring in patients with sleep apnoea. respiratory rate – root mean square error The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring Correspondence of respiration in patients with sleep apnoea. Rajkumar Palaniappan, MSc, AI-Rehab Aims: To develop a model to detect the respiratory phases present in the pulmo- Research Group, Universiti Malaysia Perlis (UniMAP), KampusPauh Putra, 02600 Perlis, nary acoustic signals and to evaluate the performance of the model in detecting the Malaysia. respiratory phases. Tel: +6049767399 Methods: Normalised averaged power spectral density for each frame and change Fax: +6049767399 in normalised averaged power spectral density between the adjacent frames were email: prkmect@gmail.com fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance Received: 16 August 2014 of both Mamdani and Sugeno methods, correlation coefficient and root mean Revision requested: 11 November 2014 Accepted: 07 December 2014 square error (RMSE) were calculated. Results: In TI - A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system JF - The Clinical Respiratory Journal DO - 10.1111/crj.12250 DA - 2016-07-01 UR - https://www.deepdyve.com/lp/wiley/a-novel-approach-to-detect-respiratory-phases-from-pulmonary-acoustic-ut1DBe5rWm SP - 486 EP - 494 VL - 10 IS - 4 DP - DeepDyve ER -