Deep Learning against COVID-19: Respiratory Insufficiency Detection in Brazilian Portuguese Speech

Autor(es)

Casanova, Edresson ; Gris, Lucas ; Camargo, Augusto ; da Silva, Daniel ; Gazzola, Murilo ; Sabino, Ester ; Levin, Anna S. ; Candido, Arnaldo, Jr. ; Aluisio, Sandra ; Finger, Marcelo

• Tipo de documento: Artigo Científico
• Fonte: FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL-IJCNLP 2021; v. N/A, p. 9-pg., 2021-01-01.

Resumo

Respiratory insufficiency is a symptom that requires hospitalization. This work investigates whether it is possible to detect this condition by analyzing patient's speech samples; the analysis was performed on data collected during the first wave of the COVID-19 pandemic in 2020, and thus limited to respiratory insufficiency in COVID-19 patients. For that, a dataset was created consisting of speech emissions of both COVID-19 patients affected by respiratory insufficiency and a control group. This dataset was used to build a Convolution Neural Network to detect respiratory insufficiency using speech emission MFCC representations. Methodologically, dealing with background noise was a challenge, so we also collected background noise from COVID-19 wards where patients were located. Due to the difficulty in filtering noise without eliminating crucial information, noise samples were injected in the control group data to prevent bias. Moreover, we investigated (i) two approaches to address the duration variance of audios, and (ii) the ideal number of noise samples to inject in both patients and the control group to prevent bias and overfitting. The techniques developed reached 91.66% accuracy. Thus we validated the project's Leading Hypothesis, namely that it is possible to detect respiratory insufficiency in speech utterances, under real-life environmental conditions; we believe our results justify further enquiries into the use of automated speech analysis to support health professionals in triage procedures. (AU)

• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia
• Processo FAPESP: 20/06443-5 - Estudo Spira: sistema de detecção precoce de insuficiência respiratória por meio de análise de áudio
• Beneficiário: Marcelo Finger
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático