Tao Chao, Zhang Yu, Jiang Jack J
Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of Wisconsin Medical School, Madison, WI 53792-7375, USA.
IEEE Trans Biomed Eng. 2007 May;54(5):794-801. doi: 10.1109/TBME.2006.889182.
In this paper, a new method is proposed to extract the physiologically relevant parameters of the vocal fold mathematic model including masses, spring constants and damper constants from high-speed video (HSV) image series. This method uses a genetic algorithm to optimize the model parameters until the model and the realistic vocal folds have similar dynamic behavior. Numerical experiments theoretically test the validity of the proposed parameter estimation method. Then the validated method is applied to extract the physiologically relevant parameters from the glottal area series measured by HSV in an excised larynx model. With the estimated parameters, the vocal fold model accurately describes the vibration of the observed vocal folds. Further studies show that the proposed parameter estimation method can successfully detect the increase of longitudinal tension due to the vocal fold elongation from the glottal area signal. These results imply the potential clinical application of this method in inspecting the tissue properties of vocal fold.
本文提出了一种新方法，用于从高速视频（HSV）图像序列中提取声带数学模型的生理相关参数，包括质量、弹簧常数和阻尼常数。该方法使用遗传算法优化模型参数，直到模型与实际声带具有相似的动态行为。数值实验从理论上测试了所提出的参数估计方法的有效性。然后，将经过验证的方法应用于从切除喉模型中通过HSV测量的声门面积序列中提取生理相关参数。利用估计的参数，声带模型能够准确描述观察到的声带振动。进一步的研究表明，所提出的参数估计方法能够从声门面积信号中成功检测出声带伸长导致的纵向张力增加。这些结果暗示了该方法在检查声带组织特性方面的潜在临床应用。
