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- W4367302014 abstract "Epilepsy is one of the most common neurological disorders. In order to realize a portable and wearable epilepsy prediction device, a lightweight hardware IP core is proposed for EEG epilepsy prediction. The scheme first analyzes the epileptic EEG signal and extracts the time-frequency feature maps of interictal and preictal EEG signals. Then it reduces the network parameters using the single-channel approach to reduce the number of neurons and network complexity, and trains the lightweight epilepsy prediction convolutional neural network by combining the time-frequency feature maps. Finally, a hardware convolutional layer is designed with pipeline function, and it connect 8 layers with feature map buffer to realizes the lightweight EEG epilepsy prediction hardware IP core. Using TSMC 65 nm process, the hardware IP core is functionally verified with 87.9% prediction accuracy. The area is 2.55 mm2 and the power is 4.45 mW. Under 1V & 20 MHz operating conditions, single prediction latency is 3.29 ms and energy efficiency is 0.146 μJ/class." @default.
- W4367302014 created "2023-04-29" @default.
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- W4367302014 date "2023-07-01" @default.
- W4367302014 modified "2023-10-16" @default.
- W4367302014 title "Convolutional neural network-based lightweight hardware IP core design for EEG epilepsy prediction" @default.
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- W4367302014 doi "https://doi.org/10.1016/j.mejo.2023.105810" @default.
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