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Article

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

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Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea
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School of Electromechanical and Automotive Engineering, Yantai University, 30 Qingquan Road, Laishan District, Yantai 264005, China
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Department of Architectural Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea
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Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea
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Department of Medical and Digital Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea
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Authors to whom correspondence should be addressed.
Academic Editors: Andrea Facchinetti and Carlo Massaroni
Sensors 2021, 21(21), 7036; https://doi.org/10.3390/s21217036
Received: 29 June 2021 / Revised: 21 October 2021 / Accepted: 21 October 2021 / Published: 23 October 2021
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability. View Full-Text
Keywords: cough; pneumonia; machine-learning; artificial intelligence; long short-term memory; loudness; energy ratio cough; pneumonia; machine-learning; artificial intelligence; long short-term memory; loudness; energy ratio
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MDPI and ACS Style

Chung, Y.; Jin, J.; Jo, H.I.; Lee, H.; Kim, S.-H.; Chung, S.J.; Yoon, H.J.; Park, J.; Jeon, J.Y. Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI. Sensors 2021, 21, 7036. https://doi.org/10.3390/s21217036

AMA Style

Chung Y, Jin J, Jo HI, Lee H, Kim S-H, Chung SJ, Yoon HJ, Park J, Jeon JY. Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI. Sensors. 2021; 21(21):7036. https://doi.org/10.3390/s21217036

Chicago/Turabian Style

Chung, Youngbeen, Jie Jin, Hyun I. Jo, Hyun Lee, Sang-Heon Kim, Sung J. Chung, Ho J. Yoon, Junhong Park, and Jin Y. Jeon 2021. "Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI" Sensors 21, no. 21: 7036. https://doi.org/10.3390/s21217036

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