Abstract
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.
Highlights
Abnormal breathing sounds are usually considered as indicator symptoms in chronic respiratory diseases [1], such as chronic obstructive pulmonary disease (COPD), chronic bronchitis, and bronchial asthma, etc
In the feature pattern of healthy breathing sounds, the values of NSI0Hz–250Hz were of NSI0Hz–250Hz, NSI250Hz–500Hz, and NSI500Hz–1000Hz were used as the frequency-domain feature in similar to that of NSI250Hz–500Hz
It was shown that the value of NSI250Hz-500Hz for wheezing sounds was larger than that of NSI0Hz–250Hz and NSI500Hz–1000Hz, and this indicated that the spectral distribution of wheezing sounds mainly concentrated at a frequency range from 250 Hz to 500 Hz due to the phenomenon of bronchoconstriction
Summary
Abnormal breathing sounds (such as crackles, rhonchus, and wheezing sounds) are usually considered as indicator symptoms in chronic respiratory diseases [1], such as chronic obstructive pulmonary disease (COPD), chronic bronchitis, and bronchial asthma, etc. For these diseases, unnecessary secretions (such as sputum) would be produced in the respiratory tract and cause chronic inflammation leading to airway obstruction. The airflow velocity will be changed when the air flows from the normal airway into the narrowing airway, producing abnormal breathing sounds, such as wheezes [3]. It is important to detect wheezing sounds automatically, and to provide prompt medical treatment for patients with acute airway obstruction
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