Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube
Abstract To prevent immediate mortality in patients with a tracheostomy tube, it is essential to ensure timely suctioning or replacement of the tube. Breathing sounds at the entrance of tracheostomy tubes were recorded with a microphone and analyzed using a spectrogram to detect airway problems. The...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47904-0 |
| _version_ | 1797415158291628032 |
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| author | Hyunbum Kim Daeyeon Koh Yohan Jung Hyunjun Han Jongbaeg Kim Younghoon Joo |
| author_facet | Hyunbum Kim Daeyeon Koh Yohan Jung Hyunjun Han Jongbaeg Kim Younghoon Joo |
| author_sort | Hyunbum Kim |
| collection | DOAJ |
| description | Abstract To prevent immediate mortality in patients with a tracheostomy tube, it is essential to ensure timely suctioning or replacement of the tube. Breathing sounds at the entrance of tracheostomy tubes were recorded with a microphone and analyzed using a spectrogram to detect airway problems. The sounds were classified into three categories based on the waveform of the spectrogram according to the obstacle status: normal breathing sounds (NS), vibrant breathing sounds (VS) caused by movable obstacles, and sharp breathing sounds (SS) caused by fixed obstacles. A total of 3950 breathing sounds from 23 patients were analyzed. Despite neither the patients nor the medical staff recognizing any airway problems, the number and percentage of NS, VS, and SS were 1449 (36.7%), 1313 (33.2%), and 1188 (30.1%), respectively. Artificial intelligence (AI) was utilized to automatically classify breathing sounds. MobileNet and Inception_v3 exhibited the highest sensitivity and specificity scores of 0.9441 and 0.9414, respectively. When classifying into three categories, ResNet_50 showed the highest accuracy of 0.9027, and AlexNet showed the highest accuracy of 0.9660 in abnormal sounds. Classifying breathing sounds into three categories is very useful in deciding whether to suction or change the tracheostomy tubes, and AI can accomplish this with high accuracy. |
| first_indexed | 2024-03-09T05:44:47Z |
| format | Article |
| id | doaj.art-16f5876cefbf456597a8bdb55fa359ac |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T05:44:47Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-16f5876cefbf456597a8bdb55fa359ac2023-12-03T12:22:05ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-47904-0Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tubeHyunbum Kim0Daeyeon Koh1Yohan Jung2Hyunjun Han3Jongbaeg Kim4Younghoon Joo5Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, The Catholic University of KoreaSchool of Mechanical Engineering, Yonsei UniversitySchool of Mechanical Engineering, Yonsei UniversitySchool of Mechanical Engineering, Yonsei UniversitySchool of Mechanical Engineering, Yonsei UniversityDepartment of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, The Catholic University of KoreaAbstract To prevent immediate mortality in patients with a tracheostomy tube, it is essential to ensure timely suctioning or replacement of the tube. Breathing sounds at the entrance of tracheostomy tubes were recorded with a microphone and analyzed using a spectrogram to detect airway problems. The sounds were classified into three categories based on the waveform of the spectrogram according to the obstacle status: normal breathing sounds (NS), vibrant breathing sounds (VS) caused by movable obstacles, and sharp breathing sounds (SS) caused by fixed obstacles. A total of 3950 breathing sounds from 23 patients were analyzed. Despite neither the patients nor the medical staff recognizing any airway problems, the number and percentage of NS, VS, and SS were 1449 (36.7%), 1313 (33.2%), and 1188 (30.1%), respectively. Artificial intelligence (AI) was utilized to automatically classify breathing sounds. MobileNet and Inception_v3 exhibited the highest sensitivity and specificity scores of 0.9441 and 0.9414, respectively. When classifying into three categories, ResNet_50 showed the highest accuracy of 0.9027, and AlexNet showed the highest accuracy of 0.9660 in abnormal sounds. Classifying breathing sounds into three categories is very useful in deciding whether to suction or change the tracheostomy tubes, and AI can accomplish this with high accuracy.https://doi.org/10.1038/s41598-023-47904-0 |
| spellingShingle | Hyunbum Kim Daeyeon Koh Yohan Jung Hyunjun Han Jongbaeg Kim Younghoon Joo Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube Scientific Reports |
| title | Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| title_full | Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| title_fullStr | Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| title_full_unstemmed | Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| title_short | Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| title_sort | breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube |
| url | https://doi.org/10.1038/s41598-023-47904-0 |
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