Literature DB >> 23000725

A new modality using breath sound analysis to evaluate the control level of asthma.

Chizu Habukawa1, Katsumi Murakami, Noriaki Horii, Maki Yamada, Yukio Nagasaka.   

Abstract

BACKGROUND: Reliable symptom assessment is essential in asthma management. We developed new technology for analyzing breath sounds and assessed its clinical usefulness for monitoring asthmatic children.
METHODS: Eighty asthmatic children and 59 non-asthmatic children underwent breath sound analysis in an asymptomatic state. Their asthma control was assessed by the Asthma Control TestTM or Childhood ACTTM scores and divided into two groups, namely, well-controlled (perfect) (n = 19) and not well-controlled (not perfect) (n = 61). Breath sounds were recorded using two sensors, located on the right anterior chest and trachea. We calculated the acoustic transfer characteristics between the two points, which indicated the relationship between frequencies and attenuation during breath sound propagation. Two indices of sound parameters, the chest wall sound index (CWI) and the tracheal sound index (TRI), were calculated from the transfer characteristics and tracheal sounds. We also developed a new parameter, the breath sound index (BSI), on a 2-dimensional diagram of CWI and TRI and tried to determine whether BSI may clarify asthma control better than CWI or TRI alone.
RESULTS: There was a significant difference in TRI and BSI between asthmatic and non-asthmatic children (p = 0.007, p < 0.001). There was a significant difference in CWI and TRI between the well-controlled and not-well-controlled groups (p < 0.001). BSI discriminated between the two groups accurately (p < 0.001). The sensitivity and specificity of BSI for asthma control were 83.6% and 84.2%, respectively.
CONCLUSIONS: Asthma control could be evaluated using a new index calculated from breath sound analysis.

Entities:  

Mesh:

Year:  2012        PMID: 23000725     DOI: 10.2332/allergolint.12-OA-0428

Source DB:  PubMed          Journal:  Allergol Int        ISSN: 1323-8930            Impact factor:   5.836


  2 in total

1.  Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.

Authors:  Jinglong Niu; Yan Shi; Maolin Cai; Zhixin Cao; Dandan Wang; Zhaozhi Zhang; Xiaohua Douglas Zhang
Journal:  Bioinformatics       Date:  2018-03-01       Impact factor: 6.937

2.  A Novel Method for Automatic Identification of Breathing State.

Authors:  Jinglong Niu; Maolin Cai; Yan Shi; Shuai Ren; Weiqing Xu; Wei Gao; Zujin Luo; Joseph M Reinhardt
Journal:  Sci Rep       Date:  2019-01-14       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.