Respiratory sound classification : effect of heart sound
Singapore has known to be a country with rising amount of population and coupled with a backdrop of ageing population. This proved to be a stress and puts a toll on the medical infrastructure. In a recent study conducted by the Ministry of health, it was revealed that the 2nd most prominent cause fo...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/149351 |
| _version_ | 1826113411758424064 |
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| author | Koh, Yong Hui |
| author2 | Ser Wee |
| author_facet | Ser Wee Koh, Yong Hui |
| author_sort | Koh, Yong Hui |
| collection | NTU |
| description | Singapore has known to be a country with rising amount of population and coupled with a backdrop of ageing population. This proved to be a stress and puts a toll on the medical infrastructure. In a recent study conducted by the Ministry of health, it was revealed that the 2nd most prominent cause for death in Singapore is due to Pneumonia, just behind cancer. [1] Infants and young children, people over the age of 65, and people with chronic disease are at greatest risk of developing severe pneumonia which can be life-threatening. [2]
Although there might be other methods such as X-ray and CT scan to aid in the detection of any presence of water in the lungs, these can be pretty big and costly, which implies that it might not be easily available to everyone. Hence, this presents the need for research in automatic detection approach to anaylazing various lung sounds and attempt to classify them on the distinctive features of theirs. This would then help to better improve accuracy of the detection for these lung related diseases.
With that in mind, the project tried to study and analyse the performance of involvement of signal processing together with machine learning. The data collected will be audio samples collected from patients in Tan Tock Seng Hospital with the aid of professionals. Features extraction, features selection, and model classification will then be utilized. As raw data in audio format cannot be understand by models directly, this required the conversion into representable format of music, and thus audio is passed through Mel-Frequency Cepstrum Coefficients to enable features extraction. The extracted features are then manually selected in a way in which will contribute the most to prediction output. This will then be passed into Support Vector Machine to aid in analyzing the selected features. It will then be evaluated based on accuracy, sensitivity and specificity. |
| first_indexed | 2024-10-01T03:23:09Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149351 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:23:09Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1493512023-07-07T18:11:19Z Respiratory sound classification : effect of heart sound Koh, Yong Hui Ser Wee School of Electrical and Electronic Engineering ewser@ntu.edu.sg Engineering::Electrical and electronic engineering Singapore has known to be a country with rising amount of population and coupled with a backdrop of ageing population. This proved to be a stress and puts a toll on the medical infrastructure. In a recent study conducted by the Ministry of health, it was revealed that the 2nd most prominent cause for death in Singapore is due to Pneumonia, just behind cancer. [1] Infants and young children, people over the age of 65, and people with chronic disease are at greatest risk of developing severe pneumonia which can be life-threatening. [2] Although there might be other methods such as X-ray and CT scan to aid in the detection of any presence of water in the lungs, these can be pretty big and costly, which implies that it might not be easily available to everyone. Hence, this presents the need for research in automatic detection approach to anaylazing various lung sounds and attempt to classify them on the distinctive features of theirs. This would then help to better improve accuracy of the detection for these lung related diseases. With that in mind, the project tried to study and analyse the performance of involvement of signal processing together with machine learning. The data collected will be audio samples collected from patients in Tan Tock Seng Hospital with the aid of professionals. Features extraction, features selection, and model classification will then be utilized. As raw data in audio format cannot be understand by models directly, this required the conversion into representable format of music, and thus audio is passed through Mel-Frequency Cepstrum Coefficients to enable features extraction. The extracted features are then manually selected in a way in which will contribute the most to prediction output. This will then be passed into Support Vector Machine to aid in analyzing the selected features. It will then be evaluated based on accuracy, sensitivity and specificity. Bachelor of Engineering (Information Engineering and Media) 2021-05-30T13:00:45Z 2021-05-30T13:00:45Z 2021 Final Year Project (FYP) Koh, Y. H. (2021). Respiratory sound classification : effect of heart sound. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149351 https://hdl.handle.net/10356/149351 en A3204-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Koh, Yong Hui Respiratory sound classification : effect of heart sound |
| title | Respiratory sound classification : effect of heart sound |
| title_full | Respiratory sound classification : effect of heart sound |
| title_fullStr | Respiratory sound classification : effect of heart sound |
| title_full_unstemmed | Respiratory sound classification : effect of heart sound |
| title_short | Respiratory sound classification : effect of heart sound |
| title_sort | respiratory sound classification effect of heart sound |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/149351 |
| work_keys_str_mv | AT kohyonghui respiratorysoundclassificationeffectofheartsound |