Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings
Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings
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DOI：
10.1038/s41598-020-65492-1
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发表时间：
2020-05-26
影响因子：
4.6
通讯作者：
Sejdic, Ervin
中科院分区：
文献类型：
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作者：
Khalifa, Yassin;Coyle, James L.;Sejdic, Ervin
高分辨率颈部听诊是一种非常有前途的吞咽困难筛查和误吸检测的无创方法，因为它不涉及使用有害的电离辐射方法。颈部听诊吞咽事件的自动提取是吞咽分析在临床上有效的关键步骤。使用吞咽信号的时变谱估计和深度前馈神经网络，我们提出了一种用于吞咽加速度计和声音的自动分割算法，该算法以在线方式直接作用于原始吞咽信号。使用从248名患者收集的吞咽数据对该算法进行了定性和定量验证，产生了由经验丰富的语言病理学家手动标记的3000多个吞咽。该算法的检测准确率超过95%，与现有算法相比表现出上级性能，并在测试来自不同人群的76只完全看不见的燕子时证明了其普适性。所提出的方法不仅是非常重要的任何后续的吞咽信号分析步骤，但也提供了一个证据，这样的信号可以捕获的吞咽过程的生理特征。
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.