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The Effect of Sampling Rate on the Extraction of VEP Features Using Wavelet Transform
Zakaria H.a, Ahmad M.a
a Biomedical Engineering Department, School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.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. Visual Evoked Potential (VEP) is a method that can be used to early detect this disease where the eye will be given a visual stimulus, and the response will be recorded through the Oz point in the back of the head. The recorded signal is then processed to obtain the VEP features that are used clinically as a reference for assessing eye conditions, namely the P75, P100 and P145 responses. This study was conducted to increase the accuracy of the VEP signal feature extraction process by using quadratic biorthogonal b-splines wavelet by varying the sampling rate of signal acquisition. To optimize the feature extraction process, 5 different sampling rates were chosen, namely 288, 256, 224, 192, and 160 for the data retrieval process. Result showed that sampling rate of 256 was optimal. Higher sampling rate was found to provide similar accuracy.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Extraction process,Optic neuritis,Recorded signals,Signal acquisitions,Signal features,Visual evoked potential,Visual stimulus,Wavelet[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]EEG,Visual Evoked Potential,Wavelet[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISITIA.2019.8937268[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]