A Novel Method for Automatic Identification of Breathing State
Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated s...
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| Published in | Scientific reports Vol. 9; no. 1; p. 103 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
14.01.2019
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-018-36454-5 |
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| Abstract | Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient
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s trachea. |
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| AbstractList | Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient
’
s trachea. Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea.Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea. Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient's trachea. |
| ArticleNumber | 103 |
| Author | Xu, Weiqing Luo, Zujin Niu, Jinglong Gao, Wei Shi, Yan Reinhardt, Joseph M. Cai, Maolin Ren, Shuai |
| Author_xml | – sequence: 1 givenname: Jinglong surname: Niu fullname: Niu, Jinglong organization: School of Automation Science and Electrical Engineering, Beihang University, Department of Biomedical Engineering, University of Iowa – sequence: 2 givenname: Maolin surname: Cai fullname: Cai, Maolin organization: School of Automation Science and Electrical Engineering, Beihang University – sequence: 3 givenname: Yan surname: Shi fullname: Shi, Yan email: yesoyou@buaa.edu.cn organization: School of Automation Science and Electrical Engineering, Beihang University – sequence: 4 givenname: Shuai surname: Ren fullname: Ren, Shuai organization: School of Automation Science and Electrical Engineering, Beihang University – sequence: 5 givenname: Weiqing surname: Xu fullname: Xu, Weiqing organization: School of Automation Science and Electrical Engineering, Beihang University – sequence: 6 givenname: Wei surname: Gao fullname: Gao, Wei email: gaow13975@163.com organization: Department of Respiration, Beijing Anzhen Hospital, Capital Medical University – sequence: 7 givenname: Zujin surname: Luo fullname: Luo, Zujin organization: Department of Respiratory and Critical Care Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital,Capital Medical University – sequence: 8 givenname: Joseph M. surname: Reinhardt fullname: Reinhardt, Joseph M. organization: Department of Biomedical Engineering, University of Iowa |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30643176$$D View this record in MEDLINE/PubMed |
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| Snippet | Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated... |
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| SubjectTerms | 692/4019/2776 692/4019/592 Airway Obstruction - diagnosis Auscultation Automation - methods Classification Critical care Feature selection Humanities and Social Sciences Humans Intubation, Intratracheal - adverse effects Medical research Microphones multidisciplinary Noise Patients Respiration Respiratory Sounds Science Science (multidisciplinary) Sensitivity and Specificity Sensors Signal Processing, Computer-Assisted Sound Sputum Trachea Ventilators Workloads |
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| Title | A Novel Method for Automatic Identification of Breathing State |
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