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Signal shape feature for automatic snore and breathing sounds classification

机译：信号形状功能可自动打sn和呼吸音分类

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摘要

Snore analysis techniques have recently been developed for sleep studies. Most snore analysis techniques require reliable methods for the automatic classification of snore and breathing sounds in the sound recording. In this study we focus on this problem and propose an automated method to classify snore and breathing sounds based on the novel feature, 'positiveegative amplitude ratio (PNAR)', to measure the shape of the sound signal. The performance of the proposed method was evaluated using snore and breathing recordings (snore: 22 643 episodes and breathing: 4664 episodes) from 40 subjects. Receiver operating characteristic (ROC) analysis showed that the proposed method achieved 0.923 sensitivity with 0.918 specificity for snore and breathing sound classification on test data. PNAR has substantial potential as a feature in the front end of a non-contact snore/breathing-based technology for sleep studies.
机译：打ore分析技术最近已被开发用于睡眠研究。大多数打sn分析技术需要可靠的方法来自动分类录音中的打ore和呼吸声。在这项研究中，我们着眼于这个问题，并基于“正/负振幅比（PNAR）”这一新颖功能，提出了一种自动分类打sn和呼吸声的方法，以测量声音信号的形状。使用来自40名受试者的打sn和呼吸记录（打no：22 643集，呼吸：4664集）评估了所提出方法的性能。接收器工作特性（ROC）分析表明，该方法对打data和呼吸声分类的测试数据的灵敏度达到0.923，特异性为0.918。 PNAR在基于非接触打sn /呼吸技术的睡眠研究前端具有巨大潜力。

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