COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT
Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automati...
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| Format: | Article |
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University of Baghdad, College of Science for Women
2023-04-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6516 |
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| author | Thabit Sultan Mohammed Awni Ismail Sultan Khattab M. Ali Alheeti Karim Mohammed Aljebory Hasan Ismail Sultan Muzhir Shaban Al-Ani |
| author_facet | Thabit Sultan Mohammed Awni Ismail Sultan Khattab M. Ali Alheeti Karim Mohammed Aljebory Hasan Ismail Sultan Muzhir Shaban Al-Ani |
| author_sort | Thabit Sultan Mohammed |
| collection | DOAJ |
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Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests. The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people.
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| format | Article |
| id | doaj-art-b4ae04c207774cd0988751c034e9e282 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-b4ae04c207774cd0988751c034e9e2822025-08-20T03:35:57ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862023-04-0120210.21123/bsj.2022.6516COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWTThabit Sultan Mohammed0Awni Ismail Sultan1Khattab M. Ali Alheeti2Karim Mohammed Aljebory3Hasan Ismail Sultan4Muzhir Shaban Al-Ani5Computer Technical Engineering Department, Al-Qalam University College, Kirkuk, Iraq.F.I.C.M.S, Department of Internal Medicine, College of Medicine, Tikrit University, Tikrit, Iraq.College of Computers and Information Technology, University of Anbar, Anbar, Iraq. Computer Technical Engineering Department, Al-Qalam University College, Kirkuk, Iraq.F.I.C.M.S, Department of Internal Medicine, College of Medicine, Tikrit University, Tikrit, Iraq.Department of IT, College of Science and Technology, University of Human Development, Sulaymaniyah, Iraq. Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests. The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6516Corona Virus, Cough Sound, COVID-19, DWT, Feature Extraction, Signal Processing, Statistical Analysis, SVD |
| spellingShingle | Thabit Sultan Mohammed Awni Ismail Sultan Khattab M. Ali Alheeti Karim Mohammed Aljebory Hasan Ismail Sultan Muzhir Shaban Al-Ani COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT مجلة بغداد للعلوم Corona Virus, Cough Sound, COVID-19, DWT, Feature Extraction, Signal Processing, Statistical Analysis, SVD |
| title | COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT |
| title_full | COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT |
| title_fullStr | COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT |
| title_full_unstemmed | COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT |
| title_short | COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT |
| title_sort | covid 19 diagnosis using spectral and statistical analysis of cough recordings based on the combination of svd and dwt |
| topic | Corona Virus, Cough Sound, COVID-19, DWT, Feature Extraction, Signal Processing, Statistical Analysis, SVD |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6516 |
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