Design of wearable breathing sound monitoring system for real-time wheeze detection

Shih Hong Li, Bor Shing Lin, Chen Han Tsai, Cheng Ta Yang, Bor-Shyh Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.

Original languageEnglish
Article number171
JournalSensors (Switzerland)
Issue number1
StatePublished - 17 Jan 2017


  • Airway obstruction
  • Short-term breathing sound
  • Spectral integration
  • Wheeze detection


Dive into the research topics of 'Design of wearable breathing sound monitoring system for real-time wheeze detection'. Together they form a unique fingerprint.

Cite this