In this study, the development of an automatic algorithm is presented to classify the nocturnal audio recording of an obstructive sleep apnoea (OSA) patient as OSA related snore, simple snore and other sounds. Recent studies has been shown that knowledge regarding the OSA related snore could assist in identifying the site of airway collapse. Audio signal was recorded simultaneously with full-night polysomnography during sleep with a ceiling microphone. Time and frequency features of the nocturnal audio signal were extracted to classify the audio signal into OSA related snore, simple snore and other sounds. Two algorithms were developed to extract OSA related snore using an linear discriminant analysis (LDA) classifier based on the hypothesis that OSA related snoring can assist in identifying the site-of-upper airway collapse. An unbiased nested leave-one patient-out cross-validation process was used to select a high performing feature set from the full set of features. Results indicated that the algorithm achieved an accuracy of 87% for identifying snore events from the audio recordings and an accuracy of 72% for identifying OSA related snore events from the snore events. The direct method to extract OSA-related snore events using a multi-class LDA classifier achieved an accuracy of 64% using the feature selection algorithm. Our results gives a clear indication that OSA-related snore events can be extracted from nocturnal sound recordings, and therefore could potentially be used as a new tool for identifying the site of airway collapse from the nocturnal audio recordings.

中文翻译：

---

从夜间录音自动分类OSA相关的打nor信号

在这项研究中，提出了一种自动算法的开发，以将阻塞性睡眠呼吸暂停（OSA）患者的夜间录音分类为与OSA相关的打ore，简单打sn和其他声音。最近的研究表明，有关OSA相关打sn的知识可以帮助识别气道塌陷的部位。睡眠期间使用天花板麦克风同时记录音频信号和整夜的多导睡眠监测仪。提取夜间音频信号的时间和频率特征，以将音频信号分类为与OSA相关的打ore，简单打sn和其他声音。基于以下假设，开发了两种算法来使用线性判别分析（LDA）分类器提取与OSA相关的打sn：OSA相关的打可以帮助识别上呼吸道塌陷的位置。使用无偏嵌套的“请假一人出诊”交叉验证过程从整个功能集中选择一个高性能的功能集。结果表明，该算法从录音中识别打sn事件的准确度达到87％，从打sn事件中识别OSA相关打sn事件的准确度达到72％。使用多类LDA分类器提取OSA相关打sn事件的直接方法，使用特征选择算法可达到64％的准确度。我们的结果清楚地表明，可以从夜间录音中提取与OSA相关的打sn事件，因此有可能被用作从夜间录音中识别气道塌陷部位的新工具。