Silent aspiration detection by breath and swallowing sound analysis
Silent aspiration detection by breath and swallowing sound analysis
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通过呼吸和吞咽声音分析进行无声误吸检测
DOI：
10.1109/embc.2012.6346496
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Z. Moussavi
中科院分区：
文献类型：
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作者：
Samaneh Sarraf Shirazi;Z. Moussavi
检测吞咽后的吸入(药丸进入气管)通常是一项困难的任务，特别是当患者不咳嗽的时候；这被称为静默吸入。本研究旨在探讨声学分析技术在无声吸气检测中的应用。我们记录了10名患有吞咽障碍的患者的吞咽和呼吸声，他们在纤维内窥镜评估吞咽(费用)时表现出无声吸气。我们分析了每次吞咽后呼吸声信号的功率谱密度(PSD)；吸气后呼吸声的PSD在低频时表现出更高的幅度。因此，我们将300赫兹以下的频率范围划分为3个子带，并在子带上计算平均功率作为分类的特征。然后，使用模糊k-均值无监督分类方法来寻找数据集中的两个聚类：送气组和非送气组。使用言语语言病理学家提供的费用评估对结果进行评估。结果表明，静默吸入法检测燕子的准确率为82.3%。虽然提出的方法应该在更大的数据集上进行验证，但结果很有希望将声学分析用作检测静默抽吸的临床工具。
Detecting aspiration after swallows (the entry of bolus into trachea) is often a difficult task particularly when the patient does not cough; those are called silent aspiration. In this study, the application of acoustical analysis in detecting silent aspiration is investigated. We recorded the swallowing and the breath sounds of 10 individuals with swallowing disorders, who demonstrated silent aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) assessment. We analyzed the power spectral density (PSD) of the breath sound signals following each swallow; the PSD showed higher magnitude at low frequencies for the breath sounds following an aspiration. Therefore, we divided the frequency range below 300 Hz into 3 sub-bands, over which we calculated the average power as the characteristic features for the classification purpose. Then, the fuzzy k-means unsupervised classification method was deployed to find the two clusters in the data set: the aspirated and non-aspirated groups. The results were evaluated using the FEES assessments provided by the speech language pathologists. The results show 82.3% accuracy in detecting swallows with silent aspiration. Although the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.