Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment

牛海军; 冯安吉; 万明习; 白培瑞

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Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment

Updated：2026-03-12

- Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment
- Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment
- 解放军医学杂志（英文版） 2003年第1期页码：60-64
- Affiliations：
  
  1. Department of Biomedical Engineering
  2. Xi’an Jiaotong University
  3. Faculty of Preventive Medicine
  4. Fourth Military Medical University
  5. Xi’an Jiaotong University Xi’an,710049
  6. ,Xi’an,710033
- Author bio：
- Funds：
  
  Supported by Obligatory Budget of Chine PLA in the “tenth-five years”（OIL077）
- DOI：
  中图分类号： R443.4
- 纸质出版：2003
- Accepted：
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Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment[J]. 解放军医学杂志（英文版）, 2003,(1):60-64.

[1]牛海军,冯安吉,万明习,白培瑞.Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment[J].Journal of Medical Colleges of PLA,2003(01):60-64.
Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment[J]. 解放军医学杂志（英文版）, 2003,(1):60-64. DOI：

[1]牛海军,冯安吉,万明习,白培瑞.Lung sounds auscultation technology based on ANC - ICA algorithm in high bat- tlefield noise environment[J].Journal of Medical Colleges of PLA,2003(01):60-64. DOI：

摘要

Abstract

<正> AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment. METHODS: In this study

we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis （ ANC-ICA）

the method

by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence. RESULTS: The algorithm retained the local feature of lung sounds while eliminating high background noise

and performed more effectively than the conventional LMS algorithm. CONCLUSION: This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.

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