利用COVID-19患者呼吸声音的最佳声学特征

M. G. M. Milani, M. Ramashini, Krishani Murugiah, Lanka Geeganage Shamaan Chamal
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摘要

当前,世界正面临新冠肺炎大流行带来的极端危机。新冠肺炎疫情干扰世界经济和社会因素;因此,许多国家陷入贫困。此外,他们缺乏这一领域的专业知识,无法努力进行必要的聚合酶链反应(PCR)或其他昂贵的实验室测试。因此,寻找一种低成本方法替代COVID-19感染者早期预测的解决方案非常重要。本研究的目的是通过呼吸声音检测COVID-19感染者。为了完成这项任务,提取了22个声学特征。通过特征工程方法,从这些特征中识别出每个COVID-19感染呼吸声的最佳特征。提出的特征工程方法是一个混合模型,包括;统计特征评估,PCA和k-均值聚类技术。该最优声学特征工程(OAFE)模型的最终结果表明,呼吸声信号的峰度特征可以更有效地区分COVID-19感染者和健康个体。
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Exploiting optimum acoustic features in COVID-19 individual's breathing sounds
The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.
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