Djurdjanovic D, Widmalm S E, Williams W J, Koh C K, Yang K P
School of Mechanical and Production Engineering, Nanyang Technological University, Singapore.
IEEE Trans Biomed Eng. 2000 Aug;47(8):977-84. doi: 10.1109/10.855924.
Sounds, such as clicking and/or crepitation, evoked in the temporomandibular (jaw) joint during function may indicate pathology. Analysis of the reduced interference time-frequency distribution of these sounds is of diagnostic value. However, visual evaluation is expensive and error prone, and there is, thus, a need for automated analysis. The aim of this study was to find the optimal signal representation and pattern recognition method for computerized classification of temporomandibular joint sounds. Concepts of time-shift invariance with and without scale invariance were employed and mutually compared. The automated analysis methods provided classification results that were similar to previous visual classification of the sounds. It was found that the classifier performance was significantly improved when scale invariance was omitted. This behavior occurred because scale invariance interfered with the frequency content of the signal. Therefore, scale invariance should not be pursued in the classification scheme employed in this study.
在颞下颌(颌)关节功能活动期间诱发的诸如咔哒声和/或摩擦音等声音可能表明存在病变。对这些声音的降干扰时间 - 频率分布进行分析具有诊断价值。然而,视觉评估成本高且容易出错,因此需要进行自动分析。本研究的目的是找到用于颞下颌关节声音计算机分类的最佳信号表示和模式识别方法。采用了具有和不具有尺度不变性的时移不变性概念并进行相互比较。自动分析方法提供的分类结果与先前对这些声音的视觉分类相似。研究发现,当省略尺度不变性时,分类器性能得到显著提高。出现这种情况是因为尺度不变性干扰了信号的频率成分。因此,在本研究采用的分类方案中不应追求尺度不变性。