Shi C S, Mao Y
Prosthodontic Department, Stomatological College, Fourth Military Medical University, Xian Shaanxi, China.
J Oral Rehabil. 1993 Jul;20(4):373-8. doi: 10.1111/j.1365-2842.1993.tb01620.x.
This was an investigation to determine the feasibility of an autoregressive (AR) model for establishing characteristic parameters from recorded occlusal sounds and develop their classification. Thirty four normal subjects with intact natural dentitions were selected for the study. The subjects' occlusal sounds from both sides of their faces respectively were sampled, and the gnathosonic classification (Class A, B and C) was established by observing the original recorded wave pattern and measuring the duration. Then, a 20 order AR model was calculated with the collected data, and the AR model coefficients were found to be similar to the indices of Bayes' discriminatory analysis. The total conformation rates of the modelled left and right occlusal sounds to the classification, estimated by Bayes' discriminant functions were 97.06% and 88.24% respectively. AR coefficients representing the characteristics of human occlusal sounds can be helpful in their classification and allow computer diagnosis of occlusal disorders.
这是一项旨在确定自回归（AR）模型用于从记录的咬合声音中建立特征参数并进行分类的可行性的研究。本研究选取了34名天然牙列完整的正常受试者。分别采集受试者面部两侧的咬合声音，并通过观察原始记录的波形图和测量持续时间来建立颌声分类（A类、B类和C类）。然后，利用收集到的数据计算20阶AR模型，发现AR模型系数与贝叶斯判别分析指标相似。通过贝叶斯判别函数估计，模拟的左右咬合声音对分类的总符合率分别为97.06%和88.24%。代表人类咬合声音特征的AR系数有助于对其进行分类，并可实现咬合紊乱的计算机诊断。
