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Audio-based snore detection using deep neural networks
Computer Methods and Programs in Biomedicine ( IF 6.1 ) Pub Date : 2020-12-25 , DOI: 10.1016/j.cmpb.2020.105917
Jiali Xie , Xavier Aubert , Xi Long , Johannes van Dijk , Bruno Arsenali , Pedro Fonseca , Sebastiaan Overeem

Background and Objective: Snoring is a prevalent phenomenon. It may be benign, but can also be a symptom of obstructive sleep apnea (OSA) a prevalent sleep disorder. Accurate detection of snoring may help with screening and diagnosis of OSA.

Methods: We introduce a snore detection algorithm based on the combination of a convolutional neural network (CNN) and a recurrent neural network (RNN). We obtained audio recordings of 38 subjects referred to a clinical center for a sleep study. All subjects were recorded by a total of 5 microphones placed at strategic positions around the bed. The CNN was used to extract features from the sound spectrogram, while the RNN was used to process the sequential CNN output and to classify the audio events to snore and non-snore events. We also addressed the impact of microphone placement on the performance of the algorithm.

Results: The algorithm achieved an accuracy of 95.3 ± 0.5%, a sensitivity of 92.2 ± 0.9%, and a specificity of 97.7 ± 0.4% over all microphones in snore detection on our data set including 18412 sound events. The best accuracy (95.9%) was observed from the microphone placed about 70 cm above the subject's head and the worst (94.4%) was observed from the microphone placed about 130 cm above the subject's head.

Conclusion: Our results suggest that our method detects snore events from audio recordings with high accuracy and that microphone placement does not have a major impact on detection performance.



中文翻译:

使用深度神经网络的基于音频的打ore检测

背景与目的:打nor是一种普遍现象。它可能是良性的,但也可能是阻塞性睡眠呼吸暂停(OSA)的一种普遍睡眠障碍的症状。打的准确检测可能有助于OSA的筛查和诊断。

方法:我们介绍了一种基于卷积神经网络(CNN)和递归神经网络(RNN)组合的打ore检测算法。我们获得了转诊到临床中心进行睡眠研究的38位受试者的录音。所有受试者均通过位于床周围关键位置的5个麦克风进行记录。CNN用于从声谱图中提取特征,而RNN用于处理顺序CNN输出并将音频事件分类为打sn和非打events事件。我们还解决了麦克风放置对算法性能的影响。

结果:在我们对包括18412个声音事件的数据集进行打sn检测时,该算法在所有麦克风上的打sn检测均达到95.3±0.5%的准确性,92.2±0.9%的灵敏度以及97.7±0.4%的特异性。从放置在距受试者头部约70 cm处的麦克风观察到最佳准确性(95.9%),从放置在距头部约130 cm处的麦克风观察到最差的准确性(94.4%)。

结论:我们的结果表明,我们的方法可以高精度地检测音频记录中的打sn事件,并且麦克风的放置不会对检测性能产生重大影响。

更新日期:2021-01-10
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