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Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform-Based
Gregory Furman1, Evgeny Furman2, Artem Charushin3
1Physics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Computer analysis of respiratory sounds can diagnose COVID-19. Two new methods use high-frequency Fast Fourier Transform (FFT) spectra for accurate, noninvasive COVID-19 screening.
Area of Science:
- Medical Diagnostics
- Bioacoustics
- Computational Medicine
Background:
- Respiratory sounds offer insights into health and behavior.
- Characteristic sound changes are associated with COVID-19, enabling diagnostic potential.
Purpose of the Study:
- Develop two rapid, remote, computer-assisted diagnostic methods.
- Identify specific acoustic phenomena linked to COVID-19 using respiratory sound analysis.
Main Methods:
- Applied Fast Fourier Transform (FFT) to analyze respiratory sound recordings.
- Focused on the high-frequency spectrum (2000-6000 Hz) of FFT.
- Utilized recordings from hospitalized patients and home-based self-testing via mobile phones.
Main Results:
- Developed two computer-assisted diagnostic methods for COVID-19.
- Established numerical criteria distinguishing healthy individuals from COVID-19 patients.
- Diagnostic criteria demonstrated independence from age and gender.
Conclusions:
- The proposed methods enable automatic diagnosis of COVID-19 based on respiratory sound FFT spectra.
- Achieved high diagnostic values for the developed computer-assisted methods.
- Potential for creating noninvasive, self-administered screening kits for COVID-19.
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