Machine learning algorithm for sleep study
This project involves implementation of machine learning algorithm for sleep study. It aims to diagnose Obstructive Sleep Apnea (OSA) by implementing a machine learning algorithm. The standard and conventional diagnosis of sleep disorder is Polysomnography (PSG), also known as sleep study. During th...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/71742 |
| _version_ | 1811696186755645440 |
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| author | Guo, Shuli |
| author2 | Ser Wee |
| author_facet | Ser Wee Guo, Shuli |
| author_sort | Guo, Shuli |
| collection | NTU |
| description | This project involves implementation of machine learning algorithm for sleep study. It aims to diagnose Obstructive Sleep Apnea (OSA) by implementing a machine learning algorithm. The standard and conventional diagnosis of sleep disorder is Polysomnography (PSG), also known as sleep study. During the process of PSG, various bio-signals were collected as parameters to diagnose sleep disorder syndromes. However, using PSG involves the analysis of huge amount of data, which is time consuming. Therefore, the objective of this project is to study and develop a machine learning based algorithm that is able to analyze data automatically to perform sleep disorder diagnosis. As snore parameters are essential factors to predict sleep disorder, hence snoring sound recorded at National University Hospital (NUH) PSG laboratory is used as the data for this project.
With this objective, the machine learning algorithm was developed in three stages including feature extraction, feature selection and classification. Features such as formants frequency, Mel-frequency cepstral coefficients (MFCCs), energy were extracted, and then, fisher’s ratio coefficients were calculated to select the features, lastly, classification was done by using support vector machine(SVM). 78.3% accuracy was obtained from the classification learner in the result. MATLAB Scripts were programmed for implementing the whole project. In conclusion, the features used are discriminating and the performance of the classification learner is satisfying. |
| first_indexed | 2024-10-01T07:35:22Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/71742 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:35:22Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/717422023-07-07T16:09:31Z Machine learning algorithm for sleep study Guo, Shuli Ser Wee School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This project involves implementation of machine learning algorithm for sleep study. It aims to diagnose Obstructive Sleep Apnea (OSA) by implementing a machine learning algorithm. The standard and conventional diagnosis of sleep disorder is Polysomnography (PSG), also known as sleep study. During the process of PSG, various bio-signals were collected as parameters to diagnose sleep disorder syndromes. However, using PSG involves the analysis of huge amount of data, which is time consuming. Therefore, the objective of this project is to study and develop a machine learning based algorithm that is able to analyze data automatically to perform sleep disorder diagnosis. As snore parameters are essential factors to predict sleep disorder, hence snoring sound recorded at National University Hospital (NUH) PSG laboratory is used as the data for this project. With this objective, the machine learning algorithm was developed in three stages including feature extraction, feature selection and classification. Features such as formants frequency, Mel-frequency cepstral coefficients (MFCCs), energy were extracted, and then, fisher’s ratio coefficients were calculated to select the features, lastly, classification was done by using support vector machine(SVM). 78.3% accuracy was obtained from the classification learner in the result. MATLAB Scripts were programmed for implementing the whole project. In conclusion, the features used are discriminating and the performance of the classification learner is satisfying. Bachelor of Engineering 2017-05-19T02:30:54Z 2017-05-19T02:30:54Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71742 en Nanyang Technological University 54 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Guo, Shuli Machine learning algorithm for sleep study |
| title | Machine learning algorithm for sleep study |
| title_full | Machine learning algorithm for sleep study |
| title_fullStr | Machine learning algorithm for sleep study |
| title_full_unstemmed | Machine learning algorithm for sleep study |
| title_short | Machine learning algorithm for sleep study |
| title_sort | machine learning algorithm for sleep study |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/71742 |
| work_keys_str_mv | AT guoshuli machinelearningalgorithmforsleepstudy |