Ischemic Stroke Detection System With Computer Aided Diagnostic Capability
Ischemic stroke is caused by narrowing of the blood vessel due to emboli travel along the blood vessel that eventually trapped near the vessel wall and become stenosis. Transcranial Doppler (TCD) Ultrasound is used as a tool to detect emboli. However, the TCD monitoring process is time-consuming and...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2017
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| Online Access: | http://eprints.usm.my/53074/1/Ischemic%20Stroke%20Detection%20System%20With%20Computer%20Aided%20Diagnostic%20Capability_Lina%20Tay_E3_2017.pdf |
| _version_ | 1797012940336922624 |
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| author | Lina, Tay |
| author_facet | Lina, Tay |
| author_sort | Lina, Tay |
| collection | USM |
| description | Ischemic stroke is caused by narrowing of the blood vessel due to emboli travel along the blood vessel that eventually trapped near the vessel wall and become stenosis. Transcranial Doppler (TCD) Ultrasound is used as a tool to detect emboli. However, the TCD monitoring process is time-consuming and fatigue. Since the evaluation requires human experts, limited number of experts makes the manual emboli detection a challenging task. Therefore, this project is to develop program for automated emboli detection. MATLAB are used to develop signal processing algorithm of the system. In this project, there are four detection methods investigated. The first method is sinusoidal modelling method where the frequency spectrum were inspected to search for the frequency components with high magnitude. The second method compares the energy and zero crossing rate of embolic signal with the threshold level. Subsequently, the short time energy and short time average zero crossing rate method is employed to compare two characteristic with threshold level computed. The last method is the Support Vector Machine (SVM) classifier where Mel Frequency Cepstral Coefficients (MFCC) is the extracted features used to train the classifier. The performance evaluations of the detection methods are measured by accuracy percentage and processing time. The best result is achieved by the sinusoidal modelling method with high genuine acceptance rate at 84.2% and low false rejection rate of 33.14%. After the proposed software system is validated, the system is modified and employed into a graphical user interface (GUI) application. |
| first_indexed | 2024-03-06T15:55:04Z |
| format | Monograph |
| id | usm.eprints-53074 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:55:04Z |
| publishDate | 2017 |
| publisher | Universiti Sains Malaysia |
| record_format | dspace |
| spelling | usm.eprints-530742022-06-25T08:20:23Z http://eprints.usm.my/53074/ Ischemic Stroke Detection System With Computer Aided Diagnostic Capability Lina, Tay T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Ischemic stroke is caused by narrowing of the blood vessel due to emboli travel along the blood vessel that eventually trapped near the vessel wall and become stenosis. Transcranial Doppler (TCD) Ultrasound is used as a tool to detect emboli. However, the TCD monitoring process is time-consuming and fatigue. Since the evaluation requires human experts, limited number of experts makes the manual emboli detection a challenging task. Therefore, this project is to develop program for automated emboli detection. MATLAB are used to develop signal processing algorithm of the system. In this project, there are four detection methods investigated. The first method is sinusoidal modelling method where the frequency spectrum were inspected to search for the frequency components with high magnitude. The second method compares the energy and zero crossing rate of embolic signal with the threshold level. Subsequently, the short time energy and short time average zero crossing rate method is employed to compare two characteristic with threshold level computed. The last method is the Support Vector Machine (SVM) classifier where Mel Frequency Cepstral Coefficients (MFCC) is the extracted features used to train the classifier. The performance evaluations of the detection methods are measured by accuracy percentage and processing time. The best result is achieved by the sinusoidal modelling method with high genuine acceptance rate at 84.2% and low false rejection rate of 33.14%. After the proposed software system is validated, the system is modified and employed into a graphical user interface (GUI) application. Universiti Sains Malaysia 2017-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53074/1/Ischemic%20Stroke%20Detection%20System%20With%20Computer%20Aided%20Diagnostic%20Capability_Lina%20Tay_E3_2017.pdf Lina, Tay (2017) Ischemic Stroke Detection System With Computer Aided Diagnostic Capability. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik & Elektronik. (Submitted) |
| spellingShingle | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Lina, Tay Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title | Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title_full | Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title_fullStr | Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title_full_unstemmed | Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title_short | Ischemic Stroke Detection System With Computer Aided Diagnostic Capability |
| title_sort | ischemic stroke detection system with computer aided diagnostic capability |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | http://eprints.usm.my/53074/1/Ischemic%20Stroke%20Detection%20System%20With%20Computer%20Aided%20Diagnostic%20Capability_Lina%20Tay_E3_2017.pdf |
| work_keys_str_mv | AT linatay ischemicstrokedetectionsystemwithcomputeraideddiagnosticcapability |