Analysis of respiratory sounds
Pneumonia currently accounts for 20.6 percent of total death and is ranked 2nd under the top 10 principal causes of death in Singapore [1]. Respiratory sounds are able to provide important information for the diagnosis of lung diseases such as Pneumonia and Chronic Obstructive Pulmonary Disease (CO...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138988 |
| _version_ | 1811683515202273280 |
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| author | Chen, Dewei |
| author2 | Ser Wee |
| author_facet | Ser Wee Chen, Dewei |
| author_sort | Chen, Dewei |
| collection | NTU |
| description | Pneumonia currently accounts for 20.6 percent of total death and is ranked 2nd under the top 10 principal causes of death in Singapore [1]. Respiratory sounds are able to provide important information for the diagnosis of lung diseases such as Pneumonia and Chronic Obstructive Pulmonary Disease (COPD). Adventitious respiratory sounds such as stridor, crackle and wheeze are considered abnormal lung sounds. Physicians will usually conduct physical examination through the use of stethoscope to detect any abnormal lung sounds before conducting further evaluation through Magnetic Resonance Imaging (MRI), X-ray or Computed Tomography (CT) scan. Such examinations are often lengthy and rely heavily on the physician’s experience. In today’s world, the advancement in technology is disrupting and driving digital transformation in the healthcare industry. Hence, this prompts for research in automatic detection based method to analyse the characteristics of various lung sounds and classify them based on their distinctive features. Such automated detection based method aims to simplify and improve the accuracy of the diagnosis process for lung diseases. This study provides comprehensive analysis on relevant signal processing techniques and machine learning models to develop a multi-classification algorithm that can be implemented on a simple device. The algorithm will be able to classify and identify 4 different lung sounds namely healthy, stridor, crackle and wheeze lung sound. The development process for this project is carried out in 3 main stages; Feature Extraction, Feature Selection and Model Classification. |
| first_indexed | 2024-10-01T04:13:57Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138988 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:13:57Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1389882023-07-07T18:41:29Z Analysis of respiratory sounds Chen, Dewei Ser Wee School of Electrical and Electronic Engineering ewser@ntu.edu.sg Engineering::Electrical and electronic engineering Pneumonia currently accounts for 20.6 percent of total death and is ranked 2nd under the top 10 principal causes of death in Singapore [1]. Respiratory sounds are able to provide important information for the diagnosis of lung diseases such as Pneumonia and Chronic Obstructive Pulmonary Disease (COPD). Adventitious respiratory sounds such as stridor, crackle and wheeze are considered abnormal lung sounds. Physicians will usually conduct physical examination through the use of stethoscope to detect any abnormal lung sounds before conducting further evaluation through Magnetic Resonance Imaging (MRI), X-ray or Computed Tomography (CT) scan. Such examinations are often lengthy and rely heavily on the physician’s experience. In today’s world, the advancement in technology is disrupting and driving digital transformation in the healthcare industry. Hence, this prompts for research in automatic detection based method to analyse the characteristics of various lung sounds and classify them based on their distinctive features. Such automated detection based method aims to simplify and improve the accuracy of the diagnosis process for lung diseases. This study provides comprehensive analysis on relevant signal processing techniques and machine learning models to develop a multi-classification algorithm that can be implemented on a simple device. The algorithm will be able to classify and identify 4 different lung sounds namely healthy, stridor, crackle and wheeze lung sound. The development process for this project is carried out in 3 main stages; Feature Extraction, Feature Selection and Model Classification. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-14T08:31:30Z 2020-05-14T08:31:30Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138988 en A3192-191 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Chen, Dewei Analysis of respiratory sounds |
| title | Analysis of respiratory sounds |
| title_full | Analysis of respiratory sounds |
| title_fullStr | Analysis of respiratory sounds |
| title_full_unstemmed | Analysis of respiratory sounds |
| title_short | Analysis of respiratory sounds |
| title_sort | analysis of respiratory sounds |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/138988 |
| work_keys_str_mv | AT chendewei analysisofrespiratorysounds |