title: An alternative respiratory sounds classification system utilizing artificial neural networks.
abstract: BACKGROUND
  Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills.
  
  
  METHODS
  This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification.
  
  
  RESULTS
  The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches.
  
  
  CONCLUSIONS
  The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
authors.affiliation: Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid, Jordan.
authors.forename: Rami J
authors.initials: RJ
authors.lastname: Oweis
authors.forename: Enas W
authors.initials: EW
authors.lastname: Abdulhay
authors.forename: Amer
authors.initials: A
authors.lastname: Khayal
authors.forename: Areen
authors.initials: A
authors.lastname: Awad
mesh_terms: D001185:Artificial Intelligence; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D012135:Respiratory Sounds / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D012984:Software