دورية أكاديمية
Voice Features of Sustained Phoneme as COVID-19 Biomarker.
| العنوان: | Voice Features of Sustained Phoneme as COVID-19 Biomarker. |
|---|---|
| المؤلفون: | Pah ND; Department of Electrical EngineeringUniversitas Surabaya Surabaya 60293 Indonesia., Indrawati V; Department of Electrical EngineeringUniversitas Surabaya Surabaya 60293 Indonesia., Kumar DK; School of EngineeringRMIT University Melbourne VIC 3000 Australia. |
| المصدر: | IEEE journal of translational engineering in health and medicine [IEEE J Transl Eng Health Med] 2022 Sep 20; Vol. 10, pp. 4901309. Date of Electronic Publication: 2022 Sep 20 (Print Publication: 2022). |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101623153 Publication Model: eCollection Cited Medium: Internet ISSN: 2168-2372 (Electronic) Linking ISSN: 21682372 NLM ISO Abbreviation: IEEE J Transl Eng Health Med Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013- |
| مواضيع طبية MeSH: | COVID-19*, Humans ; Cross-Sectional Studies ; Longitudinal Studies ; Pandemics ; SARS-CoV-2 ; Biomarkers |
| مستخلص: | Background: The COVID-19 pandemic has resulted in enormous costs to our society. Besides finding medicines to treat those infected by the virus, it is important to find effective and efficient strategies to prevent the spreading of the disease. One key factor to prevent transmission is to identify COVID-19 biomarkers that can be used to develop an efficient, accurate, noninvasive, and self-administered screening procedure. Several COVID-19 variants cause significant respiratory symptoms, and thus a voice signal may be a potential biomarker for COVID-19 infection. Aim: This study investigated the effectiveness of different phonemes and a range of voice features in differentiating people infected by COVID-19 with respiratory tract symptoms. Method: This cross-sectional, longitudinal study recorded six phonemes (i.e., /a/, /e/, /i/, /o/, /u/, and /m/) from 40 COVID-19 patients and 48 healthy subjects for 22 days. The signal features were obtained for the recordings, which were statistically analyzed and classified using Support Vector Machine (SVM). Results: The statistical analysis and SVM classification show that the voice features related to the vocal tract filtering (e.g., MFCC, VTL, and formants) and the stability of the respiratory muscles and lung volume (Intensity-SD) were the most sensitive to voice change due to COVID-19. The result also shows that the features extracted from the vowel /i/ during the first 3 days after admittance to the hospital were the most effective. The SVM classification accuracy with 18 ranked features extracted from /i/ was 93.5% (with F1 score of 94.3%). Conclusion: A measurable difference exists between the voices of people with COVID-19 and healthy people, and the phoneme /i/ shows the most pronounced difference. This supports the potential for using computerized voice analysis to detect the disease and consider it a biomarker. |
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| فهرسة مساهمة: | Keywords: COVID-19; support vector machine; sustained phoneme; voice features |
| المشرفين على المادة: | 0 (Biomarkers) |
| SCR Organism: | SARS-CoV-2 variants |
| تواريخ الأحداث: | Date Created: 20221028 Date Completed: 20221031 Latest Revision: 20221121 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9592047 |
| DOI: | 10.1109/JTEHM.2022.3208057 |
| PMID: | 36304844 |
| قاعدة البيانات: | MEDLINE |
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