Chronic respiratory care and rehabilitation in France

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach

The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.

Risk Assessment for Self Reported Obstructive Sleep Apnea and Excessive Daytime Sleepiness in a Greek Nursing Staff Population

Background and objectives: The risk assessment of Obstructive Sleep Apnea (OSA) and Excessive Daytime Sleepiness (EDS) in specific occupational populations is important due to its association with morbidity. The aim of the present study was to identify the risk of OSA development and EDS in a Greek nursing staff population. Materials and Methods: In this cross-sectional study a total of 444 nurses, 56 males (age = 42.91 ± 5.76 years/BMI = 27.17 ± 4.32) and 388 females (age = 41.41 ± 5.92 years/BMI = 25.08 ± 4.43) working in a Greek secondary and tertiary hospital participated during the period from 18 January 2015 to 10 February 2015. The participants completed the Berlin Questionnaire (BQ), concerning the risk for OSA and the Epworth Sleepiness Scale (ESS), concerning the EDS. The work and lifestyle habits of the participants were correlated with the results of the questionnaires. Results: According to the BQ results 20.5% (n = 91) of the nursing staff was at high risk for OSA. Increased daytime sleepiness affected 27.7% (n = 123) of the nurses according to ESS results. Nurses at risk for Obstructive Sleep Apnea Syndrome (OSAS), positive for both BQ and ESS, were 7.66% (n = 34). Out of the nurses that participated 77% (n = 342) were working in shifts status and had significant meal instability (breakfast p < 0.0001, lunch p < 0.0001, dinner p = 0.0008). Conclusions: The population at high risk for OSA and EDS in the nursing staff was found to be 20% and 28% respectively. High risk for OSAS was detected in 7.66% of the participants. The high risk for OSA and EDS was the same irrespective of working in shift status. In specific, nursing population age was an independent predictor for high risk for OSA and skipping lunch an independent predictor of daytime sleepiness.