Mouawad Pauline, Dubnov Tammuz, Dubnov Shlomo
Lebanese American University, Byblos, Lebanon.
Zuzor, San Diego, CA USA.
SN Comput Sci. 2021;2(1):34. doi: 10.1007/s42979-020-00422-6. Epub 2021 Jan 12.
COVID-19, otherwise known as the coronavirus, has precipitated the world into a pandemic that has infected, as of the time of writing, more than 10 million persons worldwide and caused the death of more than 500,000 persons. Early symptoms of the virus include trouble breathing, fever and fatigue and over 60% of people experience a dry cough. Due to the devastating impact of COVID-19 and the tragic loss of lives, it is of the utmost urgency to develop methods for the early detection of the disease that may help limit its spread as well as aid in the development of targeted solutions. Coughs and other vocal sounds contain pulmonary health information that can be used for diagnostic purposes, and recent studies in chaotic dynamics have shown that nonlinear phenomena exist in vocal signals. The present work investigates the use of symbolic recurrence quantification measures with MFCC features for the automatic detection of COVID-19 in cough sounds of healthy and sick individuals. Our performance evaluation reveals that our symbolic dynamics measures capture the complex dynamics in the vocal sounds and are highly effective at discriminating sick and healthy coughs. We apply our method to sustained vowel 'ah' recordings, and show that our model is robust for the detection of the disease in sustained vowel utterances as well. Furthermore, we introduce a robust novel method of informative undersampling using information rate to deal with the imbalance in our dataset, due to the unavailability of an equal number of sick and healthy recordings. The proposed model achieves a mean classification performance of 97% and 99%, and a mean -score of 91% and 89% after optimization, for coughs and sustained vowels, respectively.
2019冠状病毒病（COVID-19），也就是人们熟知的冠状病毒，已使全球陷入一场大流行。截至撰写本文时，全球已有超过1000万人感染，超过50万人死亡。该病毒的早期症状包括呼吸急促、发热和疲劳，超过60%的人会出现干咳。由于COVID-19造成的毁灭性影响和生命的悲惨损失，开发早期检测该疾病的方法迫在眉睫，这可能有助于限制其传播，并有助于开发针对性的解决方案。咳嗽和其他声音包含可用于诊断目的的肺部健康信息，最近关于混沌动力学的研究表明，声音信号中存在非线性现象。本研究探讨了结合梅尔频率倒谱系数（MFCC）特征的符号递归量化测度在自动检测健康人和病人咳嗽声中COVID-19方面的应用。我们的性能评估表明，我们的符号动力学测度能够捕捉声音中的复杂动力学，并且在区分患病咳嗽和健康咳嗽方面非常有效。我们将我们的方法应用于持续元音“啊”的录音，并表明我们的模型在检测持续元音发音中的疾病方面也很稳健。此外，由于无法获得数量相等的患病和健康录音，我们引入了一种使用信息率的稳健新颖的信息欠采样方法来处理我们数据集中的不平衡问题。所提出的模型在优化后，对于咳嗽声和持续元音，分别实现了97%和99%的平均分类性能，以及91%和89%的平均F1分数。
