Respiratory sound classification : signal quality analysis
As a major disease leading to death in recent years, Pulmonary Edema disease is difficult to treat in many parts of the world. Its diagnosis and treatment difficulties are mainly reflected in the low efficiency of the diagnosis and treatment method, the diagnosis and treatment accuracy, and the disc...
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sg-ntu-dr.10356-1490922023-07-07T18:13:34Z Respiratory sound classification : signal quality analysis Tang, See Kah Ser Wee School of Electrical and Electronic Engineering ewser@ntu.edu.sg Engineering::Electrical and electronic engineering As a major disease leading to death in recent years, Pulmonary Edema disease is difficult to treat in many parts of the world. Its diagnosis and treatment difficulties are mainly reflected in the low efficiency of the diagnosis and treatment method, the diagnosis and treatment accuracy, and the discomfort brought to patients during the treatment. Given the technical limitations of the way the world currently treats such conditions, it is extremely important and urgent for the quality of signals to be controlled accurately. This study uses MATLAB programming, basic knowledge of signal processing, and machine learning. All audio sample files were recorded from volunteers, which assure the study conducted with real data and results are effective. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-26T12:12:00Z 2021-05-26T12:12:00Z 2021 Final Year Project (FYP) Tang, S. K. (2021). Respiratory sound classification : signal quality analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149092 https://hdl.handle.net/10356/149092 en A3206-201 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Tang, See Kah Respiratory sound classification : signal quality analysis |
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As a major disease leading to death in recent years, Pulmonary Edema disease is difficult to treat in many parts of the world. Its diagnosis and treatment difficulties are mainly reflected in the low efficiency of the diagnosis and treatment method, the diagnosis and treatment accuracy, and the discomfort brought to patients during the treatment. Given the technical limitations of the way the world currently treats such conditions, it is extremely important and urgent for the quality of signals to be controlled accurately.
This study uses MATLAB programming, basic knowledge of signal processing, and machine learning. All audio sample files were recorded from volunteers, which assure the study conducted with real data and results are effective. |
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Ser Wee |
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Ser Wee Tang, See Kah |
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Final Year Project |
author |
Tang, See Kah |
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Tang, See Kah |
title |
Respiratory sound classification : signal quality analysis |
title_short |
Respiratory sound classification : signal quality analysis |
title_full |
Respiratory sound classification : signal quality analysis |
title_fullStr |
Respiratory sound classification : signal quality analysis |
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Respiratory sound classification : signal quality analysis |
title_sort |
respiratory sound classification : signal quality analysis |
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Nanyang Technological University |
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2021 |
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https://hdl.handle.net/10356/149092 |
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