从夜间音频记录中自动分类与OSA相关的打鼾信号
Automatic Classification of OSA related Snoring Signals from Nocturnal Audio Recordings
摘要：
在这项研究中，提出了一种自动算法的开发，以分类阻塞性睡眠呼吸暂停（OSA）患者的夜间音频记录为OSA相关打鼾，简单打鼾和其他声音。最近的研究已经表明，关于OSA相关打鼾的知识可以帮助识别气道塌陷的部位。在睡眠期间，使用天花板麦克风与整夜多导睡眠图同时记录音频信号。提取夜间音频信号的时间和频率特征，将音频信号分类为OSA相关打鼾、单纯打鼾和其他声音。基于OSA相关打鼾可以帮助识别上气道塌陷部位的假设，开发了两种算法来使用线性判别分析（LDA）分类器提取OSA相关打鼾。使用无偏嵌套留一患者交叉验证过程从全特征集中选择高性能特征集。结果表明，该算法实现了87%的准确度为识别打鼾事件从音频记录和72%的准确度为识别睡眠呼吸暂停相关打鼾事件从打鼾事件。使用多类LDA分类器提取OSA相关打鼾事件的直接方法使用特征选择算法实现了64%的准确度。我们的研究结果清楚地表明，OSA相关的打鼾事件可以从夜间录音中提取，因此可以潜在地用作从夜间录音中识别气道塌陷部位的新工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
