The Remote Analysis of Breath Sound in Covid-19 Patients: A Series of Clinical Cases

Abstract

Background: Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate of characteristic sound changes caused by COVID-19 and can be used for diagnosis of this illness. Purpose: The communication aim is development of fast remote computer-assistance diagnosis of COVID-19, based on analysis of respiratory sounds. Materials and Methods: Fast Fourier transform (FFT) was applied for analyses of respiratory sounds recorded near the mouth of 9 COVID-19 patients and 4 healthy volunteers. Sampling rate was 48 kHz. Results: Comparing of FFT spectrums of the respiratory sounds of the patients and volunteers we proposed numerical healthy-ill criterions. Conclusions: The proposed computer method, based on analysis of the FFT spectrums of respiratory sounds of the patients and volunteers, allows one to automatically diagnose COVID-19 with sufficiently high diagnostic values. This method can be applied at development of noninvasive self-testing kits for COVID-19.

AI-Based human audio processing for COVID-19: A comprehensive overview

“…A preliminary analysis of the sound signals of respiration from nine C19 patients and four healthy volunteers is done by Furman et al. [42] using FFT harmonics. The respiration sounds are recorded using a smartphone microphone.…”

Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview

“…The feasibility of capturing and analysing breathing sounds using smartphone microphones to even understand COVID-19 symptoms as a part of a personal analytics is discussed in [63]. A preliminary analysis of the sound signals of respiration from 9 COVID-19 patients and 4 healthy volunteers is executed in [64] using FFT harmonics. The respiration sounds are recorded using a smartphone microphone.…”