BACKGROUND:Snoring source analysis is essential for an appropriate surgical decision for both simple snorers and obstructive sleep apnea/hypopnea syndrome (OSAHS) patients. OBJECTIVE:As snoring sounds carry significant information about tissue vibrations within the upper airway, a new feature entitled compressed histogram of oriented gradients (CHOG) is proposed to recognize vibration patterns of the snoring source acoustically by compressing histogram of oriented gradients (HOG) descriptors via the multilinear principal component analysis (MPCA) algorithm. METHODS:Each vibration pattern corresponds to a sole or combinatorial vibration among the four upper airway soft tissues of soft palate, lateral pharyngeal wall, tongue base, and epiglottis. 1037 snoring events from noncontact sound recordings of 76 simple snorers or OSAHS patients during drug-induced sleep endoscopy (DISE) were evaluated. RESULTS:With a support vector machine (SVM) as the classifier, the proposed CHOG achieved a recognition accuracy of 89.8% for the seven observable vibration patterns of the snoring source categorized in our most recent work. CONCLUSION:The CHOG outperforms other single features widely used for acoustic analysis of sole vibration site.

中文翻译：

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使用定向梯度的压缩直方图进行打pattern源分析的振动模式识别。

背景：打source来源分析对于简单打nor者和阻塞性睡眠呼吸暂停/呼吸不足综合征（OSAHS）患者的适当手术决策至关重要。目的：由于打nor声会携带有关上呼吸道内组织振动的大量信息，因此提出了一种名为“压缩定向梯度直方图（CHOG）”的新功能，该特征可通过压缩定向梯度直方图（HOG）描述符通过声学方式识别打the源的振动模式多线性主成分分析（MPCA）算法。方法：每个振动模式对应于软pa，咽旁壁，舌根和会厌的四个上呼吸道软组织中的单一或组合振动。评价了76例简单打nor者或OSAHS患者的非接触式录音中的1037个打nor事件，这些事件来自于药物诱发的睡眠内窥镜检查（DISE）。结果：使用支持向量机（SVM）作为分类器，拟议的CHOG对我们最新工作中所分类的打source声源的七个可观察到的振动模式实现了89.8％的识别精度。结论：CHOG优于其他广泛用于鞋底振动部位声学分析的单一功能。