Adaptive Neuro-Fuzzy Inference System For Breath Phase Detection And Breath Cycle Segmentation

Sundaraj, Kenneth and Palaniappan, Rajkumar and Sundaraj, Sebastian (2017) Adaptive Neuro-Fuzzy Inference System For Breath Phase Detection And Breath Cycle Segmentation. Computer Methods And Programs In Biomedicine, 145. pp. 67-72. ISSN 0169-2607

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The monitoring of the respiratory rate is vital in several medical conditions,including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls.Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.Objectives:This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.Methods:The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated.The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation.To evaluate the performance of the proposed method,the root mean square error (RMSE) and correlation coefficient values were calculated and analysed,and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.Results:The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance,revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.

Item Type: Article
Uncontrolled Keywords: Breath phase detectionSegmentationRespiratory sound signalNeuro-fuzzyCorrelation coefficientRoot mean square errorRespiratory rate
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Electronics and Computer Engineering > Department of Industrial Electronics
Depositing User: Mohd. Nazir Taib
Date Deposited: 31 Jan 2019 02:53
Last Modified: 05 Jul 2021 11:39
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