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源自人类颞下颌关节声音的特征描述。

Characterization of sounds emanating from the human temporomandibular joints.

作者信息

Prinz J F, Ng K W

机构信息

Department of Anatomy, University of Hong Kong, Hong Kong.

出版信息

Arch Oral Biol. 1996 Jul;41(7):631-9. doi: 10.1016/s0003-9969(96)00070-2.

Abstract

Sounds from the temporomandibular joint were recorded on audiotape from 238 individuals by placing microphones in both ears. The recordings were later digitized at a sample rate of 1.7 kHz with 10-bit resolution and stored on computer disk. At least two open-close cycles were assessed from each individual; 2707 different individual sounds were analysed in the time and frequency domains. The sounds were classified as: (a) single, short duration (clicks), (b) multiple, short-duration (creaks) and (c) long duration (crepitus). The sounds were further subclassified into either high or low amplitude by (i) the attack, which produced hard and soft categories and (ii) comparing the amplitude between sides-bilateral sounds were those with amplitudes differing by < 40 mV; the rest were unilateral. To establish the robustness of the classification 42 acoustic events were selected to be classified visually by three observers on two separate occasions. Intraobserver agreement was 82% (kappa = 0.75) while interobserver agreement was 60% (kappa = 0.71). Statistically significant differences were noted between all classifications of sound. These were most marked in the time domain. A simple, automated classification scheme was devised that was capable of categorizing the sounds with 82% agreement (kappa = 0.71) compared to a human observer.

摘要

通过将麦克风置于双耳,从238名个体的颞下颌关节处录制声音并存储于录音带。随后,这些录音以1.7kHz的采样率、10位分辨率进行数字化处理,并存储在计算机磁盘上。对每个个体至少评估两个开闭周期;对2707种不同的个体声音进行时域和频域分析。声音被分类为:(a) 单个、持续时间短(咔嗒声),(b) 多个、持续时间短(嘎吱声),以及 (c) 持续时间长(摩擦音)。声音进一步根据以下方式分为高振幅或低振幅:(i) 起始部分,分为硬声和软声类别;(ii) 比较两侧之间的振幅——双侧声音是指振幅差异小于40mV的声音;其余为单侧声音。为确定分类的稳健性,选择了42个声学事件,由三名观察者在两个不同场合进行视觉分类。观察者内一致性为82%(kappa = 0.75),而观察者间一致性为60%(kappa = 0.71)。在声音的所有分类之间均发现了具有统计学意义的差异。这些差异在时域最为明显。设计了一种简单的自动分类方案,与人类观察者相比,该方案能够以82%的一致性(kappa = 0.71)对声音进行分类。

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