Verde Laura, De Pietro Giuseppe, Ghoneim Ahmed, Alrashoud Mubarak, Al-Mutib Khaled N, Sannino Giovanna
Institute of High-Performance Computing and Networking (ICAR)-National Research Council of Italy (CNR) 80131 Naples Italy.
Department of Software EngineeringCollege of Computer and Information SciencesKing Saud University Riyadh 11543 Saudi Arabia.
IEEE Access. 2021 Apr 26;9:65750-65757. doi: 10.1109/ACCESS.2021.3075571. eCollection 2021.
The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and Artificial Intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.
新冠疫情是过去几十年来最严重的全球卫生突发事件之一，给世界各国社会都带来了不可磨灭的影响。由于该病毒具有高传染性和高死亡率，生命损失代价惨重。数百万人受到感染，常常需要持续的救助和监测。智能医疗技术和人工智能算法是很有前景的解决方案，不仅有助于监测患者护理情况，还能以更快、更准确的方式支持新冠病毒的早期诊断、预防和评估。另一方面，要实现可靠、精确的智能医疗解决方案，能够通过合适的物联网设备实时采集和处理语音信号，就需要识别出能够准确区分病理状态和健康状态的算法。在本文中，我们探讨并比较了主要机器学习技术通过语音分析正确检测新冠病症的能力。事实上，多项研究报告称，由于呼吸器官受到严重损害，这种病毒对语音产生有显著影响。在新冠患者发声过程中，可观察到声带振动更加不同步、不对称且受限。我们对一个现有的众包数据库Coswara数据库中选取的语音样本进行了分析和处理，以评估主要机器学习技术区分健康语音和病理语音的能力。所有分析都从准确率、灵敏度、特异性、F1分数和受试者工作特征曲线下面积等方面进行了评估。这些结果表明支持向量机算法在检测新冠感染方面的可靠性，其准确率约为97%。
