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SCIENCE CHINA Information Sciences
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SCIENCE CHINA Information Sciences, Volume 64 , Issue 3 : 134101(2021) https://doi.org/10.1007/s11432-019-1510-6

Using breath sound data to detect intraoperative respiratory depression in non-intubated anesthesia

Sikai WANG 1,2, Kang ZHAO 3, Ming LIU 1,*, Hanjun JIANG 3, Zhihua WANG 3, Zongwang ZHANG 4, Huili KAN 4, Ping CHEN 5, Binjie ZHU 5
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  • ReceivedMay 20, 2019
  • AcceptedAug 2, 2019
  • PublishedFeb 3, 2021
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Abstract

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Acknowledgment

This work was supported by National Key Technologies RD Program (Grant No. 2017YFB0405604), Key Research Program of Frontier Science, Chinese Academy of Sciences (Grant No. QYZDY-SSW-JSC004), Basic Research Project of Shanghai Science and Technology Commission (Grant No. 16JC1400101), and Beijing ST Planning Task (Grant No. Z161100002616019).


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References

[1] Ahmed I S, Aziz E, Newton N. Connection of capnography sampling tube to an intravenous cannula.. Anaesthesia, 2005, 60: 824-825 CrossRef PubMed Google Scholar

[2] Frasca D, Geraud L, Charriere J M. Comparison of acoustic and impedance methods with mask capnometry to assess respiration rate in obese patients recovering from general anaesthesia.. Anaesthesia, 2015, 70: 26-31 CrossRef PubMed Google Scholar

[3] Rizal A, Hidayat R, Nugroho H A, et al. Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification. In: Proceedings of 2016 1st International Conference on Biomedical Engineering (IBIOMED), Yogyakarta, 2016. Google Scholar

[4] Palaniappan R, Sundaraj K, Sundaraj S. Artificial intelligence techniques used in respiratory sound analysis--a systematic review.. Biomedizinische Technik/BioMed Eng, 2014, 59 CrossRef PubMed Google Scholar

[5] Rocha B M, Filos D, Mendes L, et al. Α respiratory sound database for the development of automated classification. In: Precision Medicine Powered by pHealth and Connected Health. Berlin: Springer, 2017. 33--37. Google Scholar

[6] Palaniappan R, Sundaraj K, Sundaraj S. Artificial intelligence techniques used in respiratory sound analysis--a systematic review.. Biomedizinische Technik/BioMed Eng, 2014, 59 CrossRef PubMed Google Scholar

[7] Yin Y, Jiang H J, Feng S L. Bowel sound recognition using SVM classification in a wearable health monitoring system. Sci China Inf Sci, 2018, 61: 084301 CrossRef Google Scholar

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    Figure 1

    (Color online) (1) Algorithm flow. (2) Monitoring system architecture[5]. (3) Comparison results before and after ICA: (a) 1-minute raw signal segment power spectrum of the breath sound; (b) simultaneous segment selected after ICA processing; (c) zoom in on the selected part of (a); (d) simultaneous corresponding part of (b); (e) power spectrum of the normal breath sound template from [6]. (4) Algorithms comparison result.