Diagnosing the Stage of COVID-19 using Machine Learning on Breath Sounds

Abstract

With rapidly increasing COVID-19 cases, patients with mildand moderate symptoms are being asked to home-isolate themselvesto save hospital resources for more severe patients.Such patients have been asked to self-monitor themselves andseek medical attention if their condition worsens. COVID-19 affects the respiratory system and home-isolated patientsmust monitor their lung condition continuously before it quicklydeteriorates. But this is difficult to monitor by oneself, and thepatient may not notice his worsening lung condition before itis too late. A machine-learning based approach is proposedto monitor lung condition by analyzing the breath sounds ofa patient for respiratory sounds like wheezes, crackles andtachypnea, which in turn can identify the stage of COVID-19. Data from a respiratory sound database with recordingsfrom 226 patients was split into 6898 respiratory cycles andpre-processed. In this paper, two approaches are evaluated.The first approach is demonstrated using Google Cloud AutoMLwith the recordings of respiratory cycles which wereconverted to spectrograms to train the model. In the secondapproach, Log Mel filter-bank features were extracted fromthe breath sounds and used to train multiple CNN models tohierarchically classify breath sounds. This ensemble-learningwith hierarchical-model approach achieved a better accuracyof 78.12%. This model can be integrated with a mobile applicationto record and analyze breath-sounds. This will enablethe patient to admit himself sooner if he is progressing to asevere stage of COVID-19.