Epileptic seizure detection on an ultra-low-power embedded risc-v processor using a convolutional neural network

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t = 35 ms and consumes an average power of P ≈ 140 µW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.

Subjects

Convolutional neural network

EEG

Epileptic seizure detection

RISC-V

Ultra-low-power

DDC Class

600: Technik

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

biosensors-11-00203-v2.pdf

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1022.03 KB

Format

Adobe PDF

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Epileptic seizure detection on an ultra-low-power embedded risc-v processor using a convolutional neural network