Discrimination of healthy controls and selected visually impaired through visually evoked potentials
This thesis presents a digital signal processing based detection of healthy controls and selected visually impaired through visually evoked potentials (VEP). Visual impairment is a term used by ophthalmologist to describe any kind of vision loss, whether it's partial or total vision loss. Some...
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| Format: | Thesis |
| Language: | English |
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| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44135/1/p.%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44135/2/full%20text.pdf |
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| Summary: | This thesis presents a digital signal processing based detection of healthy controls and selected visually impaired through visually evoked potentials (VEP). Visual impairment is a term used by ophthalmologist to describe any kind of vision loss, whether it's partial
or total vision loss. Some of the conventionally used techniques for the investigation of vision impairments include fundoscopy imaging, ultrasound imaging, and manual inspection of retina. These techniques have several disadvantages such as poor quality of images produced by the ultrasound imaging, require experts, and are prone to error in manual inspection. The VEP provides an objective method for the diagnostics of vision impairments in patients. VEP is an electrical signal generated by the brain (Occipital Cortex) in response to a visual stimulus. By analyzing these responses, the abnormalities in the visual pathways of a person can be detected. The development of feature extraction and classification algorithms for investigation of vision impairments through VEPs however is still at an infancy level. Therefore, this study was carried out to investigate the time, frequency, and time-scale/frequency characteristics of the single trial transient VEPs, and propose an efficient feature extraction and classification algorithm for distinguishing the vision impairments. Four different feature extraction
methods based on time, frequency, wavelet, and Stockwell transform were explored and
statistical features were proposed for the VEP analysis. A new feature augmentation
technique was proposed to enhance the variation of the data prior to the analysis. Three
different feature reduction techniques were used to reduce the dimensional space of the
features. Extreme learning machine, least square support vector machine and
probabilistic neural networks were employed to evaluate the performance of the features
in discriminating the vision impairments. Statistical analysis were used to demonstrate
the significance of the preprocessed features, while performance measures such as
sensitivity, specificity, positive predictivity, negative predictivity, and overall accuracy
was considered for the evaluation of the classifiers. The dataset from two different
experimental settings were used in the analysis. The first experiment was conducted to
investigate the effect of different sizes of checkerboard stimulus to the resulting evoked
responses while the second experiment was perpetrated to investigate the performance
of the new colour fusioned checkerboard stimulus in elicitating reliable VEP responses.
The experimental investigation elucidate that features derived from the VEP elicited by
the proposed stimulus performed well in classifying the vision impairments. Promising
100% accuracy was achieved using the combinations of the proposed stimulus and
feature extraction methods. |
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