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Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform-Based Computer-Assisted Diagnostic Methods.
Furman, Gregory; Furman, Evgeny; Charushin, Artem; Eirikh, Ekaterina; Malinin, Sergey; Sheludko, Valery; Sokolovsky, Vladimir; Shtivelman, David.
Afiliação
  • Furman G; Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
  • Furman E; Department of Pediatric, EA Vagner Perm State Medical University, Perm, Russian Federation.
  • Charushin A; Department of Ear, Nose and Throat, EA Vagner Perm State Medical University, Perm, Russian Federation.
  • Eirikh E; Department of Ear, Nose and Throat, EA Vagner Perm State Medical University, Perm, Russian Federation.
  • Malinin S; Central Research Laboratory, EA Vagner Perm State Medical University, Perm, Russian Federation.
  • Sheludko V; Perm Regional Clinical Infectious Diseases Hospital, Perm, Russian Federation.
  • Sokolovsky V; Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
  • Shtivelman D; Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
JMIR Form Res ; 6(7): e31200, 2022 Jul 19.
Article em En | MEDLINE | ID: mdl-35584091
BACKGROUND: Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate characteristic sound changes caused by COVID-19 and can be used for diagnostics of this illness. OBJECTIVE: The aim of the study is to develop 2 fast, remote computer-assisted diagnostic methods for specific acoustic phenomena associated with COVID-19 based on analysis of respiratory sounds. METHODS: Fast Fourier transform (FFT) was applied for computer analysis of respiratory sound recordings produced by hospital doctors near the mouths of 14 patients with COVID-19 (aged 18-80 years) and 17 healthy volunteers (aged 5-48 years). Recordings for 30 patients and 26 healthy persons (aged 11-67 years, 34, 60%, women), who agreed to be tested at home, were made by the individuals themselves using a mobile telephone; the records were passed for analysis using WhatsApp. For hospitalized patients, the illness was diagnosed using a set of medical methods; for outpatients, polymerase chain reaction (PCR) was used. The sampling rate of the recordings was from 44 to 96 kHz. Unlike usual computer-assisted diagnostic methods for illnesses based on respiratory sound analysis, we proposed to test the high-frequency part of the FFT spectrum (2000-6000 Hz). RESULTS: Comparing the FFT spectra of the respiratory sounds of patients and volunteers, we developed 2 computer-assisted methods of COVID-19 diagnostics and determined numerical healthy-ill criteria. These criteria were independent of gender and age of the tested person. CONCLUSIONS: The 2 proposed computer-assisted diagnostic methods, based on the analysis of the respiratory sound FFT spectra of patients and volunteers, allow one to automatically diagnose specific acoustic phenomena associated with COVID-19 with sufficiently high diagnostic values. These methods can be applied to develop noninvasive screening self-testing kits for COVID-19.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article