EXTRACTION OF VEP SIGNAL FEATURES BY USING QUADRATIC BIORTHOGONAL B-SPLINE WAVELET
Eyes are important senses for humans. Despite its vital role, this organ is susceptible to disease. One of the disturbances that can occur is Optic Neuritis, in which the process of demyelination occurs. To detect this disease early, one method of testing is Visual Evoked Potential (VEP). VEP is...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/36250 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Eyes are important senses for humans. Despite its vital role, this organ is
susceptible to disease. One of the disturbances that can occur is Optic Neuritis, in
which the process of demyelination occurs. To detect this disease early, one
method of testing is Visual Evoked Potential (VEP). VEP is a clinical testing
method that is performed to determine brain response to visual stimulus through
EEG signal reading in the occupital lobe Oz. From these signals, the latency
values of P75, P100 and P145 will be obtained to assess visual conditions. This
research was conducted to optimize the VEP signal feature extraction process
using quadratic biorthogonal b-splines wavelet. To optimize the feature extraction
process, 5 different sampling rates were chosen, namely 256, 224, 192, and 160
for the data retrieval process. The stages in this study are the EEG circuit
calibration process that will be used for data acquisition using input from EEG
simulator with an input frequency of 5Hz and a peak amplitude of 30uV.
Furthermore, the data acquisition process with 5 different sampling rates were
taken in 13 male subject and 10 female subject. In the feature extraction stage, the
data will be filtered using a bandpass filter with a frequency band of 1-50Hz to
reduce noise. Then the data will be decomposed using wavelet transforms and
VEP signal will be reconstructed by using D3 and D4 detail levels. From the
results of the reconstruction, P75, P100 and P145 features will be obtained. The
data analysis phase is done by comparing the standard deviation of the average
results from the VEP feature extraction categorized based on the sampling rate.
From the results of the study, the smallest standard deviation is 8.93 at 256
sampling rate, and the largest standard deviation value is 13.35 at 160 sampling
rate. |
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