  Nowadays, several deep learning methods are proposed to tackle the challenge
of epileptic seizure prediction. However, these methods still cannot be
implemented as part of implantable or efficient wearable devices due to their
large hardware and corresponding high-power consumption. They usually require
complex feature extraction process, large memory for storing high precision
parameters and complex arithmetic computation, which greatly increases required
hardware resources. Moreover, available yield poor prediction performance,
because they adopt network architecture directly from image recognition
applications fails to accurately consider the characteristics of EEG signals.
We propose in this paper a hardware-friendly network called Binary
Single-dimensional Convolutional Neural Network (BSDCNN) intended for epileptic
seizure prediction. BSDCNN utilizes 1D convolutional kernels to improve
prediction performance. All parameters are binarized to reduce the required
computation and storage, except the first layer. Overall area under curve,
sensitivity, and false prediction rate reaches 0.915, 89.26%, 0.117/h and
0.970, 94.69%, 0.095/h on American Epilepsy Society Seizure Prediction
Challenge (AES) dataset and the CHB-MIT one respectively. The proposed
architecture outperforms recent works while offering 7.2 and 25.5 times
reductions on the size of parameter and computation, respectively.
