Albadr Musatafa Abbas Abbood, Tiun Sabrina, Ayob Masri, Al-Dhief Fahad Taha
CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor Malaysia.
School of Electrical Engineering, Department of Communication Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia.
Cognit Comput. 2022 Oct 12:1-16. doi: 10.1007/s12559-022-10063-x.
COVID-19 (coronavirus disease 2019) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. Recently, it has been demonstrated that the voice data of the respiratory system (i.e., speech, sneezing, coughing, and breathing) can be processed via machine learning (ML) algorithms to detect respiratory system diseases, including COVID-19. Consequently, many researchers have applied various ML algorithms to detect COVID-19 by using voice data from the respiratory system. However, most of the recent COVID-19 detection systems have worked on a limited dataset. In other words, the systems utilize cough and breath voices only and ignore the voices of the other respiratory system, such as speech and vowels. In addition, another issue that should be considered in COVID-19 detection systems is the classification accuracy of the algorithm. The particle swarm optimization-extreme learning machine (PSO-ELM) is an ML algorithm that can be considered an accurate and fast algorithm in the process of classification. Therefore, this study proposes a COVID-19 detection system by utilizing the PSO-ELM as a classifier and mel frequency cepstral coefficients (MFCCs) for feature extraction. In this study, respiratory system voice samples were taken from the Corona Hack Respiratory Sound Dataset (CHRSD). The proposed system involves thirteen different scenarios: breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowels. The experimental results demonstrated that the PSO-ELM was capable of attaining the highest accuracy, reaching 95.83%, 91.67%, 89.13%, 96.43%, 92.86%, 88.89%, 96.15%, 96.43%, 88.46%, 96.15%, 96.15%, 95.83%, and 82.89% for breath deep, breath shallow, all breath, cough heavy, cough shallow, all cough, count fast, count normal, all count, vowel a, vowel e, vowel o, and all vowel scenarios, respectively. The PSO-ELM is an efficient technique for the detection of COVID-19 utilizing voice data from the respiratory system.
2019冠状病毒病（COVID-19）是由严重急性呼吸综合征冠状病毒2引发的一场持续的全球大流行。最近，有研究表明，呼吸系统的语音数据（即说话、打喷嚏、咳嗽和呼吸声）可通过机器学习（ML）算法进行处理，以检测包括COVID-19在内的呼吸系统疾病。因此，许多研究人员已应用各种ML算法，通过使用来自呼吸系统的语音数据来检测COVID-19。然而，最近的大多数COVID-19检测系统都是在有限的数据集上运行的。换句话说，这些系统仅利用咳嗽声和呼吸声，而忽略了呼吸系统的其他声音，如说话声和元音。此外，COVID-19检测系统中应考虑的另一个问题是算法的分类准确率。粒子群优化极限学习机（PSO-ELM）是一种ML算法，在分类过程中可被视为一种准确且快速的算法。因此，本研究提出了一种COVID-19检测系统，该系统利用PSO-ELM作为分类器，并使用梅尔频率倒谱系数（MFCC）进行特征提取。在本研究中，呼吸系统语音样本取自科罗娜黑客呼吸声数据集（CHRSD）。所提出的系统涉及13种不同的场景：深呼吸、浅呼吸、所有呼吸声、剧烈咳嗽、轻微咳嗽、所有咳嗽声、快速计数、正常计数、所有计数、元音a、元音e、元音o以及所有元音。实验结果表明，PSO-ELM能够达到最高准确率，在深呼吸、浅呼吸、所有呼吸声、剧烈咳嗽、轻微咳嗽、所有咳嗽声、快速计数、正常计数、所有计数、元音a、元音e、元音o以及所有元音场景下，准确率分别达到95.83%、91.67%、89.13%、96.43%、92.86%、88.89%、96.15%、96.43%、88.46%、96.15%、96.15%、95.83%和82.89%。PSO-ELM是利用来自呼吸系统的语音数据检测COVID-19的一种有效技术。
