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dc.contributor.authorKyaw Zin Win.en_US
dc.description66 p.en_US
dc.description.abstractLung sounds have been valuable indicators of respiratory health and disease since ancient times. However, when heard through a stethoscope, the sound repertoire is rather limited. When analyzed with a computer, a much wider range of information can be obtained. Such a computer analysis reaches far beyond the capabilities of human ear. The purpose of this project is to develop a sound processing unit to differentiate breathing and airway noises. This can be applied for epidemiological clinical use and also in clinical trials involving intervention with medications. It can also be used in the sleep lab and in home screening evaluation. By segmenting the lung sound signals, it is possible to identify the different components of the input sound. Segmentation methods based on Wavelet transform and Walsh basis functions can be exploited to discriminate the different parts of the breathing sound. The proposed schemes are evaluated on sound signals from AURORA- Subset of SpeechDat-car Database. Matlab is used for simulation. The results are compared with those manually.en_US
dc.subjectDRNTU::Engineering::Mechanical engineering::Assistive technology-
dc.titleOn the use of segmentation for lung sound analysisen_US
dc.contributor.supervisorFarook Sattaren_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeMaster of Science (Biomedical Engineering)en_US
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