期刊文献+

基于呼吸音的呼吸率监测方法研究

Study on Respiratory Rate Monitoring Method Based on Breath Sounds
下载PDF
导出
摘要 该文旨在讨论使用呼吸音监测人体呼吸率的可行性。首先提取了呼吸音的平均功率信号,对其使用自相关算法计算呼吸周期。同时对鼻气流压力信号求呼吸周期,将此结果作为参考标准,之后对两组周期数据进行相关性分析以及BlandAltman分析。在呼吸速率相对稳定的情况下,使用呼吸音监测人体呼吸率的方法是可行的;在呼吸速率有明显改变的情况下,现有方法及算法暂时无法通过呼吸音准确地反映呼吸率变化的情况,还需要进一步研究。 The article aims to discuss the feasibility of using respiratory sounds to monitor respiratory rate. The average power of respiratory sounds was created firstly, the autocorrelation algorithm was used to calculate the respiratory cycle. The respiratory cycle of nasal flow pressure signal was calculated simultaneously, and the result was taken as a reference standard, then, two groups of respiratory cycle data were analyzed by correlation analysis and Bland Altman analysis. The respiratory rate is relatively stable, using respiratory sounds monitor respiratory rate is feasible, the respiratory rate changes obviously, the existing methods and algorithm using respiratory sounds are temporarily unable to accurately reflect the changes of respiratory rate, further research is needed.
作者 李文宇 刘静 于璐 LI Wenyu;LIU Jing;YU Lu(Department of Biomedical Engineering,School of Fundamental Sciences,China Medical University,Shenyang,110122)
出处 《中国医疗器械杂志》 2018年第6期391-394,共4页 Chinese Journal of Medical Instrumentation
基金 国家自然科学基金(81401485)
关键词 呼吸音 平均功率 呼吸率 Bland-Altman分析 respiratory sounds average power respiratory rate Bland Altman analysis
  • 相关文献

参考文献5

二级参考文献25

  • 1Akagi M,Iwaki M,MinakawaT.Fundamental frequency fluctuation in continuous vowel utterance and its pexception[C].Fifth International Conference on Spoken Language Processing,Sydney,1998,1519-1522.
  • 2VapnikVN.统计学习理论的本质[M].北京:清华大学出版社,2000..
  • 3ALVAREZ D, HORNERO R, MARCOS J V, et al. Apply- ing time, frequency and nonlinear features from nocturnal oximetry to OSA diagnosis [C]//30th Annual International IEEE EMBS Conference, Vancouver, Canada: 2008:3872 3875.
  • 4ALVAREZ D, HORNERO R, MARCOS J V, et al. Multi variate analysis of blood oxygen saturation recordings in ob structive sleep apnea diagnosis[J]. IEEE Trans Biomed Eng 2010, 57(12): 2816-2824.
  • 5PATANGAY A, VEMURI P, TEWFIK A. Monitoring of obstructive sleep apnea in heart failure patients[C]//Proceed- ings of 29th Annual International Conference of the IEEE EMBS, Lyon, France 2007: 1043-1046.
  • 6MAALI Y, AL-JUMAII.Y A. Automated detecting sleep ap- nea syndrome: a novel system based on genetic SVM [C]// 1 lth International Conference on Hybrid Intelligent Systems, Melacca, Malaysia: 2011:590 594.
  • 7PENZEL T, MOODY G B, MARK R G, et al. The apnea ECG database [C]//Computers in Cardiology 2000, Cam bridge: 2000; 255 258.
  • 8MOODY G B, MARK R G, GOLDBERGER A, et al. Stim ulating rapid research advances via focused competition: the computers in cardiology challenge 2000 [C]// Computers in Cardiology 2000, Cambridge: 2000: 207-210.
  • 9CHOUAKRI S A, BEREKSI-REGU1G F, TALEB-AHMED A. QRS complex detection based on multi wavelet packet de- composition [J]. Appl MathComput, 2011, 217(23): 9508- 9525.
  • 10DE CHAZAI. P, HENEGHAN C, SHERIDAN E, et al. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea[J]. IEEE Trans Bi omed Eng, 2003, 50(6):686 696.

共引文献42

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部