Sarraf Shirazi Samaneh, Moussavi Zahra
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2599-602. doi: 10.1109/EMBC.2012.6346496.
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.
检测吞咽后误吸（食团进入气管）往往是一项艰巨的任务，尤其是当患者不咳嗽时；这些被称为隐性误吸。在本研究中，对声学分析在检测隐性误吸中的应用进行了调查。我们记录了10名吞咽障碍患者的吞咽声和呼吸声，这些患者在吞咽功能的纤维内镜评估（FEES）中表现出隐性误吸。我们分析了每次吞咽后呼吸声信号的功率谱密度（PSD）；对于误吸后的呼吸声，PSD在低频处显示出更高的幅度。因此，我们将300Hz以下的频率范围分为3个子带，在这些子带上计算平均功率作为分类的特征。然后，采用模糊k均值无监督分类方法在数据集中找到两个聚类：误吸组和非误吸组。结果通过言语病理学家提供的FEES评估进行评价。结果显示，检测隐性误吸吞咽的准确率为82.3%。虽然该方法应在更大的数据集中进行验证，但这些结果对于将声学分析用作检测隐性误吸的临床工具很有前景。
