Background and purpose

Snoring is one of the sleep disorders, and snoring sounds have been used to diagnose many sleep-related diseases. However, the snoring sound classification is done manually which is time-consuming and prone to human errors. An automated snoring sound classification model is proposed to overcome these problems.

Material and method

This work proposes an automated snoring sound classification method using three new methods. These methods are maximum absolute pooling (MAP), the nonlinear present pattern, and two-layered neighborhood component analysis, and iterative neighborhood component analysis (NCAINCA) selector. Using these methods, a new snoring sound classification (SSC) model is presented. The MAP decomposition model is applied to snoring sounds to extract both low and high-level features. The presented model aims to attain high performance for SSC problem. The developed present pattern (Present-Pat) uses substitution box (SBox) and statistical feature generator. By deploying these feature generators, both textural and statistical features are generated. NCAINCA chooses the most informative/valuable features, and these selected features are fed to k-nearest neighbor (kNN) classifier with leave-one-out cross-validation (LOOCV). The Present-Pat based SSC system is developed using Munich-Passau Snore Sound Corpus (MPSSC) dataset comprising of four categories.

Results

Our model reached an accuracy and unweighted average recall (UAR) of 97.10 % and 97.60 %, respectively, using LOOCV. Moreover, a nocturnal sound dataset is used to show the universal success of the presented model. Our model attained an accuracy of 98.14 % using the used nocturnal sound dataset.

Conclusions

Our developed classification model is ready to be tested with more data and can be used by sleep specialists to diagnose the sleep disorders based on snoring sounds.

中文翻译：

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本密码代换盒在鼾声自动检测中的应用

背景和目的

打鼾是睡眠障碍之一，打鼾声音已被用于诊断许多与睡眠有关的疾病。然而，鼾声分类是手动完成的，这既耗时又容易出现人为错误。提出了一种自动鼾声分类模型来克服这些问题。

材料和方法

这项工作提出了一种使用三种新方法的自动鼾声分类方法。这些方法是最大绝对池化 (MAP)、非线性当前模式、两层邻域分量分析和迭代邻域分量分析 (NCAINCA) 选择器。使用这些方法，提出了一种新的鼾声分类 (SSC) 模型。MAP 分解模型应用于鼾声以提取低级和高级特征。所提出的模型旨在实现 SSC 问题的高性能。开发的当前模式（Present-Pat）使用替换框（SBox）和统计特征生成器。通过部署这些特征生成器，可以生成纹理和统计特征。NCAINCA 选择信息量最大/最有价值的功能，并且这些选定的特征被馈送到具有留一法交叉验证（LOOCV）的k-最近邻（kNN）分类器。基于 Present-Pat 的 SSC 系统是使用慕尼黑-帕绍鼾声语料库 (MPSSC) 数据集开发的，该数据集包含四个类别。

结果

我们的模型使用 LOOCV 分别达到了 97.10% 和 97.60% 的准确率和未加权平均召回率 (UAR)。此外，夜间声音数据集用于显示所提出模型的普遍成功。我们的模型使用使用的夜间声音数据集达到了 98.14% 的准确率。

结论

我们开发的分类模型已准备好接受更多数据的测试，可供睡眠专家根据鼾声诊断睡眠障碍。