Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation. (July 2017)

• Record Type: Journal Article
• Authors: Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
• Is Part Of: Computer methods and programs in biomedicine. Volume 145(2017)
• Page Start: 67
• Page End: 72
• Publication Date: 2017-07
• Subjects: Breath phase detection -- Segmentation -- Respiratory sound signal -- Neuro-fuzzy -- Correlation coefficient -- Root mean square error -- Respiratory rate; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.04.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: ANFIS based breath cycle detection and segmentation algorithm was implemented. ANFIS uses power spectral density from breath sounds to detect the breath phases. The ANFIS model was evaluated using root mean square error and correlation coefficient. A correlation strength of r = 0.9925, and the RMSE = 0.0069 was obtained. The proposed methods was validated using RALE database. Abstract: Background: 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. Conclusion: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Graphical abstract: