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Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients
Puspita J.W.a, Gunadharma S.c, Indratno S.W.b, Soewono E.b
a Mathematics Study Program of Science Faculty, Universitas Tadulako, Palu, 94118, Indonesia
b Department of Mathematics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
c Hasan Sadikin Bandung Hospital, Bandung, 40161, 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]© 2017 Elsevier LtdElectroencephalography (EEG) is the most common test being used to diagnose epilepsy. Most abnormal EEG patterns in epilepsy are interictal epileptiform discharges (IEDs), which consist of spike and sharp waves. These two types of waves can be detected in detail by using the Walsh transformation. In this technique, training data consisting of the original data from EEGs and the results of the first- and second-order Walsh transformation are collected to construct IED profiles. In this paper we propose two Bayesian classification models based on the dependence of the IED profiles. Bayesian classification is applied to classify spike and sharp waves resulting from the Walsh transformation. In our case study, the classification model with dependent features assumption gave better results than the model with independent features assumption.[/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]Bayesian classification,Eeg datum,Sharp waves,Spike waves,Transformation methods[/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]Bayesian classification method,EEG data,Sharp waves,Spike waves,Walsh transformation method[/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/10.1016/j.bspc.2017.02.016[/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]