论文标题

基于深度学习的ballistarcarpiongrams系统中睡眠效果状态的分类

Classification Of Sleep-Wake State In A Ballistocardiogram System Based On Deep Learning

论文作者

Ahmed, Nemath, Singh, Aashit, KS, Srivyshnav, Kumar, Gulshan, Parchani, Gaurav, Saran, Vibhor

论文摘要

睡眠状态分类对于管理和理解睡眠方式至关重要,通常是识别急性或慢性睡眠障碍的第一步。但是,必须在不影响受试者睡眠期间的自然环境或条件的情况下进行此操作。诸如多渗透学(PSG)之类的技术是令人震惊的,对于定期睡眠监测不方便。幸运的是,新型技术和高级计算的兴起使最近的睡眠技术复兴。一种这种非接触式和不引人注目的监测技术是Ballistocragraphy(BCG),其中通过测量人体对血液心脏射血的反应来监测生命力。在这项研究中,我们提出了一个基于多头1D卷积的深神经网络,以使用来自BCG传感器的信号来准确地对睡眠效果进行分类并准确预测睡眠效果时间。我们的方法达到95.5%的睡眠效果分类评分,这与基于PSG系统的研究相当。我们进一步在受控和不受控制的环境中进行了两项独立研究,以测试睡眠效果预测的准确性。在350名受试者的不受控制的环境中,在受控环境中,我们在受控环境中获得94.16%的分数。提议的系统的高精度和非接触性质使其成为长期监测睡眠状态的方便方法。

Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment or conditions of the subject during their sleep. Techniques such as Polysomnography(PSG) are obtrusive and are not convenient for regular sleep monitoring. Fortunately, The rise of novel technologies and advanced computing has given a recent resurgence to monitoring sleep techniques. One such contactless and unobtrusive monitoring technique is Ballistocradiography(BCG), in which vitals are monitored by measuring the body's reaction to the cardiac ejection of blood. In this study, we propose a Multi-Head 1D-Convolution based Deep Neural Network to classify sleep-wake state and predict sleep-wake time accurately using the signals coming from a BCG sensor. Our method achieves a sleep-wake classification score of 95.5%, which is on par with researches based on the PSG system. We further conducted two independent studies in a controlled and uncontrolled environment to test the sleep-wake prediction accuracy. We achieve a score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature of the proposed system make it a convenient method for long term monitoring of sleep states.

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