scholarly journals Neural Network Classification of EEG Signals by Using AR with MLE Preprocessing for Epileptic Seizure Detection

2005 ◽  
Vol 10 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Abdulhamit Subasi ◽  
M. Kiymik ◽  
Ahmet Alkan ◽  
Etem Koklukaya
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Eeg Signals ◽  
Epileptic Seizure Detection ◽  
Neural Network Classification

scholarly journals Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

Biosensors ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 203
Author(s):  
Andreas Bahr ◽  
Matthias Schneider ◽  
Maria Francis ◽  
Hendrik Lehmann ◽  
Igor Barg ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Convolutional Neural Network ◽  
Medical Devices ◽  
Epileptic Seizure ◽  
Average Power ◽  
Seizure Detection ◽  
Ultra Low Power ◽  
Epileptic Seizure Detection

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.

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