AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.
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.
MATERIAL AND METHODS: A prospective, quasi-experimental physiological study. Selected healthy subjects were observed electrocardiographically for 60 s continuously in three equal phases of 20 s each - baseline phase, nasoendoscopic phase, and recovery phase (post-nasoendoscopy). Heart rate fluctuations were charted, followed by identification of a positive nasocardiac reflex group of subjects and a negative group. Analyses against multiple variables were done.
RESULTS: A total of 53 subjects were analysed. Heart rate during the baseline phase was 81.0 ± 9.9, nasoendoscopic phase was 72.7 ± 10.1, and recovery phase was 75.2 ± 9.6. Sixteen subjects (30.2%) had a positive nasocardiac reflex, and they remained in sinus rhythm with no occurrences of skipped beats, atrioventricular blocks or asystoles. One subject (1.9%) developed temporary ectopic premature ventricular contractions after nasoendoscopy. No variables were found affecting the incidence of a nasocardiac reflex in our study.
CONCLUSIONS: The pattern of heart rate dynamics was consistent as heart rates drop rapidly upon endoscope insertion and recover in some measure after its withdrawal. Although all our subjects remained asymptomatic, clinicians should not overlook the risks of a severe nasocardiac reflex when performing nasoendoscopy. We recommend that electrical cardiac monitoring be part of the management of vasovagal responses during in-office endonasal procedures.