Perturbation and Nonlinear Dynamic Analysis of Adult Male Smokers

• Published in: Journal of voice Vol. 25; no. 3; pp. 342 - 347
• Main Authors: Chai, Lingying, Sprecher, Alicia J., Zhang, Yi, Liang, Yufang, Chen, Huijun, Jiang, Jack J.
• Format: Journal Article
• Language: English
• Published: United States Mosby, Inc 01.05.2011; Elsevier Science Ltd
• Subjects: Acoustic analysis; Adult; Analysis; Case-Control Studies; China; Comparative Studies; Drug Effects; Humans; Laryngoscopy; Logistic Models; Male; Middle Aged; Nonlinear dynamic analysis; Nonlinear Dynamics; Otolaryngology; Predictive Value of Tests; Prospective Studies; ROC Curve; Signal Processing, Computer-Assisted; Smoking; Smoking - adverse effects; Sound Spectrography; Speech Acoustics; Speech Pathology; Speech Production Measurement; Voice Change; Voice disorder; Voice Disorders; Voice Disorders - diagnosis; Voice Disorders - etiology; Voice Disorders - physiopathology; Voice Quality; China; Nonlinear dynamic analysis; Acoustic analysis; Voice disorder; Smoking
• ISSN: 0892-1997; 1873-4588; 1873-4588; 1557-8658
• DOI: 10.1016/j.jvoice.2010.01.006

Smoking results in a voice change, and the perception by smokers of an abnormal voice may encourage quitting behavior. Moreover, a disordered voice is often the first sign of vocal pathology. Efforts to evaluate voice have focused on classical acoustic analysis; however, nonlinear dynamic analysis has been shown to be a reliable objective method for the evaluation of voice. We compare the discriminatory ability of these two methods when applied to normal and smokers' voices. Prospective study. The study included 73 subjects, 36 nonsmokers and 37 smokers. A segment of sustained vowel production was obtained from each subject. Acoustic dimension and correlation dimension (D2) analyses were applied to the data. Results were compared with a Mann-Whitney rank sum test, logistic regression, and receiver operating characteristics (ROC) analysis. D2 values for smokers were significantly higher than D2 values for nonsmokers ( P < 0.001). Jitter and shimmer analysis showed higher values for these parameters among smokers. Logistic regression indicated a higher predictive power with D2, and ROC analysis found no significant difference between the analysis methods. This study indicated that D2 is highly sensitive to changes associated with smoking and has the potential to be implemented clinically as an indicator of abnormal voice. Further research could focus on using nonlinear dynamic analysis to create a normative database, producing standards for monitoring voice changes caused by cigarette smoking.