Crackles detection using a time-variant autoregressive model.
Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis. The coefficients of the TVAR were obtained by an adaptive filtering prediction scheme. The adaptive filter used the recursive least squares algorithm with a forgetting factor of 0.97 and the model order was four. TVAR model showed an efficiency to detect crackles over 90% even with crackles overlapping and amplitudes as low as 1.5 of the standard deviation of background lung sounds, where expert presented an efficiency around 30%. In conclusion, TVAR model is a proper alternative to detect and to provide an estimate number of fine and coarse crackles, even in presence of crackles overlapping and crackles with low amplitude, conditions where crackles detection based on time-expanded waveform-analysis reveals evident limitations.
Dorantes-Méndez
G
G
Biomedical Engineering Program, Universidad Autónoma Metropolitana, Mexico City 09340, Mexico. lupita.dorantes@gmail.com
Charleston-Villalobos
S
S
González-Camarena
R
R
Chi-Lem
G
G
Carrillo
J G
JG
Aljama-Corrales
T
T
D000465:Algorithms; D001314:Auscultation / Q000706:statistics & numerical data; D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted; D005104:Expert Testimony; D006801:Humans; D016018:Least-Squares Analysis; D012044:Regression Analysis; D012135:Respiratory Sounds / Q000175:diagnosis* / Q000502:physiology