title: Optimized Nonlinear Dynamic Analysis of Pathologic Voices With Laryngeal Paralysis Based on the Minimum Embedding Dimension.
abstract: OBJECTIVE
  The present study aims to compare the correlation dimension and second-order entropy at the minimum embedding dimension with the correlation dimension (D2) and second-order entropy (K2) based on their efficiency and accuracy in differentiating between normal and pathologic voices.
  
  
  METHODS
  The minimum embedding dimension was estimated with the Cao method. Nonlinear dynamic parameters, such as correlation dimension and second-order entropy, were used to quantitatively analyze the normal and pathologic voice samples.
  
  
  RESULTS
  The computing time of the correlation dimension and second-order entropy at the minimum embedding dimension was reduced to approximately one third of that of traditional D2and K2calculations, reflecting higher efficiency. The statistical results of linear fitting suggested that the correlation dimension was highly correlated to the correlation dimension at the minimum embedding dimension, and second-order entropy calculation was highly correlated to the second-order entropy at the minimum embedding dimension. Lastly, the results of statistical comparison proved that the correlation dimension at the minimum embedding dimension and second-order entropy at the minimum embedding dimension were able to significantly differentiate between normal and disordered voices (P <0.001).
  
  
  CONCLUSIONS
  The results suggest that the correlation dimension and second-order entropy at the minimum embedding dimension are valid analysis tools for the diagnosis of voice disorders. Additionally, the efficiency and accuracy of these parameters yield potential for clinical usage because of lower computation time than current methods.
authors.affiliation: Department of Applied Marine Physics and Engineering, College of Ocean and Earth Sciences, Xiamen University, Xiamen, Fujian, China.
authors.forename: Nanmu
authors.initials: N
authors.lastname: Huang
authors.affiliation: Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen, China; State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China. Electronic address: yuzhang@xmu.edu.cn.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Division of Otolaryngology - Head and Neck Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: William
authors.initials: W
authors.lastname: Calawerts
authors.affiliation: Division of Otolaryngology - Head and Neck Surgery, Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: Jack J
authors.initials: JJ
authors.lastname: Jiang
mesh_terms: D000162:Acoustics; D055154:Dysphonia / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D019277:Entropy; D006801:Humans; D007830:Larynx / Q000503:physiopathology*; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D013997:Time Factors; D014826:Vocal Cord Paralysis / Q000150:complications* / Q000175:diagnosis / Q000503:physiopathology; D014833:Voice Quality
keywords: Correlation dimension; Kolmogorov entropy; Laryngeal paralysis; Minimum embedding dimension; Nonlinear dynamic analysis