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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >An Effective Deep Learning Approach for Unobtrusive Sleep Stage Detection Using Microphone Sensor
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An Effective Deep Learning Approach for Unobtrusive Sleep Stage Detection Using Microphone Sensor

机译：使用麦克风传感器进行无干扰睡眠阶段检测的有效深度学习方法

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摘要

Sleep plays a vital role in good health and well-being throughout human life. A great deal of studies have been done to detect sleep stages. Most of the current sleep monitoring systems are invasive to users, e.g. requiring users to wear a device during sleep. In this paper, we use microphone to detect sleep stages including deep sleep, light sleep and rapid eye movement (REM), and propose a convolutional neural network using spectrogram as input. This paper's contribution mainly concentrates on the following two aspects: First, microphone is unobtrusive for sleep detection. Second, we build the mapping between acoustic signal and sleep stages with little manual intervention to extract features. Performance of the proposed method is validated on a realistic environmental dataset containing 52 nights of 5 participants. Experimental results show that the accuracy of sleep stages detection is superior to the representative off-the-shelf applications. Besides, we propose to utilize the attention maps to visualize acoustic data to better understand the relationship between sound and sleep stages. Experimental results show that the model has the ability to effectively reduce noises in classification by ignoring the high-frequency sounds and white noises.
机译：睡眠对于人类一生的良好健康至关重要。已经进行了大量研究以检测睡眠阶段。当前大多数睡眠监测系统对用户都是侵入性的，例如要求用户在睡眠期间佩戴设备。在本文中，我们使用麦克风来检测包括深度睡眠，轻度睡眠和快速眼动（REM）在内的睡眠阶段，并提出了一个使用频谱图作为输入的卷积神经网络。本文的贡献主要集中在以下两个方面：首先，麦克风对睡眠检测不打扰。其次，我们只需很少的人工干预就可以建立声信号与睡眠阶段之间的映射，以提取特征。在包含5个参与者的52个夜晚的真实环境数据集上验证了所提出方法的性能。实验结果表明，睡眠阶段检测的准确性优于典型的现成应用。此外，我们建议利用注意力图对声学数据进行可视化，以更好地理解声音和睡眠阶段之间的关系。实验结果表明，该模型能够通过忽略高频声音和白噪声，有效地减少分类中的噪声。

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