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A Novel Method for Automatic Identification of Breathing State
Journal article   Open access   Peer reviewed

A Novel Method for Automatic Identification of Breathing State

Jinglong Niu, Maolin Cai, Yan Shi, Shuai Ren, Weiqing Xu, Wei Gao, Zujin Luo and Joseph M Reinhardt
Scientific reports, Vol.9(1), pp.103-103
12/01/2019
DOI: 10.1038/s41598-018-36454-5
PMCID: PMC6331627
PMID: 30643176
url
https://doi.org/10.1038/s41598-018-36454-5View
Published (Version of record) Open Access

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 ’ s trachea.

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