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FPGA implementation of deep neural network for wearable pre-seizure detector on epileptic patient
Tahar C.a, Maulana D.I.a, Pandia R.R.S.a, Adiono T.a
a University Center of Excellence on Microelectronics, Institut Teknologi Bandung, Bandung, 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.Epileptic seizure prediction is one of open challenge in medical field. This paper presents the hardware design of Deep Neural Network (DNN) for Wearable Pre-seizure Predictor on Epileptic Patient. The aim of this research is to increase the performance with less power and area by implementing the network as hardware design. The neural network consists of multiple hidden layer with 80 features as the input. The simulation result of the proposed system gives the best accuracy of 74% with 90% true positive detection. The full network including training and testing is implemented on Xilinx Zynq-7000 Zybo-7 with various techniques to achieve best speed and area. Good accuracy, less power and area imply that the system could be used for wearable devices to help epileptic patient.[/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]Epileptic patients,Epileptic seizure prediction,FPGA implementations,Hardware design,Seizure detectors,Training and testing,Wearable,Wearable devices[/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]Deep neural network,Fpga,Pre-seizure detector,Pre-seizure detector; deep neural network; fpga,Wearable,Wearable[/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/ECTI-CON47248.2019.8955139[/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]