Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms

Ana Cecilia Villa-Parra; Ismael Criollo; Carlos Valadão; Leticia Silva; Yves Coelho; Lucas Lampier; Luara Rangel; Garima Sharma; Denis Delisle-Rodríguez; John Calle-Siguencia; Fernando Urgiles-Ortiz; Camilo Díaz; Eliete Caldeira; Sridhar Krishnan; Teodiano Bastos-Filho

doi:10.3390/s22124341

Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms

Ana Cecilia Villa-Parra, Ismael Criollo, Carlos Valadão, Leticia Silva, Yves Coelho, Lucas Lampier, Luara Rangel, Garima Sharma, Denis Delisle-Rodríguez, John Calle-Siguencia, Fernando Urgiles-Ortiz, Camilo Díaz, Eliete Caldeira, Sridhar Krishnan, Teodiano Bastos-Filho

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Original language	English
Article number	4341
Journal	Sensors
Volume	22
Issue number	12
DOIs	https://doi.org/10.3390/s22124341
State	Published - 1 Jun 2022

Bibliographical note

Funding Information:
Acknowledgments: The authors acknowledge the financial support from UPS/Ecuador, CAPES/Brazil and ELAP/Canada (financial support and postdoc fellow scholarship, respectively), CNPq/Brazil (Ph.D. and researcher scholarships), FACITEC/Brazil (Ph.D. scholarship), and Global Affairs Canada (Canadian scholarship). The authors also acknowledge all the volunteers from Ecuador and Brazil.

Funding Information:
Funding: This research was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/Brazil): 012/2020 and Universidad Politécnica Salesiana (UPS) (Cuenca, Ecuador).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

biomedical sensors
COVID-19
diagnosis
machine learning
respiratory diseases
telemedicine

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10.3390/s22124341

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Villa-Parra, A. C., Criollo, I., Valadão, C., Silva, L., Coelho, Y., Lampier, L., Rangel, L., Sharma, G., Delisle-Rodríguez, D., Calle-Siguencia, J., Urgiles-Ortiz, F., Díaz, C., Caldeira, E., Krishnan, S., & Bastos-Filho, T. (2022). Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms. Sensors, 22(12), Article 4341. https://doi.org/10.3390/s22124341

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abstract = "COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.",

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Villa-Parra, AC, Criollo, I, Valadão, C, Silva, L, Coelho, Y, Lampier, L, Rangel, L, Sharma, G, Delisle-Rodríguez, D, Calle-Siguencia, J , Urgiles-Ortiz, F, Díaz, C, Caldeira, E, Krishnan, S & Bastos-Filho, T 2022, 'Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms', Sensors, vol. 22, no. 12, 4341. https://doi.org/10.3390/s22124341

TY - JOUR

T1 - Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms

AU - Villa-Parra, Ana Cecilia

AU - Criollo, Ismael

AU - Valadão, Carlos

AU - Silva, Leticia

AU - Coelho, Yves

AU - Lampier, Lucas

AU - Rangel, Luara

AU - Sharma, Garima

AU - Delisle-Rodríguez, Denis

AU - Calle-Siguencia, John

AU - Urgiles-Ortiz, Fernando

AU - Díaz, Camilo

AU - Caldeira, Eliete

AU - Krishnan, Sridhar

AU - Bastos-Filho, Teodiano

PY - 2022/6/1

Y1 - 2022/6/1

N2 - COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

AB - COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

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KW - COVID-19

KW - diagnosis

KW - machine learning

KW - respiratory diseases

KW - telemedicine

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JF - Sensors

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ER -

Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms

Abstract

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Keywords

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