An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection

This research aims to develop a data-driven computationally efficient strategy for automatic cross-patient seizure detection using spatio temporal features learned from multichannel electroencephalogram (EEG) time-series data. In this approach, we utilize an algorithm that seeks to capture spectral, temporal, and spatial information in order to achieve high generalization. This algorithm's initial step is to convert EEG signals into a series of temporal and multi-spectral pictures. The produced images are then sent into a convolutional neural network (CNN) as inputs. Our convolutional neural network as a deep learning method learns a general spatially irreducible representation of a seizure to improves sensitivity, specificity, and accuracy results comparable to the state-of-the-art results. In this work, in order to avoid the inherent high computational cost of CNNs while benefiting from their superior classification performance, a neuromorphic computing strategy for seizure prediction called spiking CNN is developed from the traditional CNN method, which is motivated by the energy-efficient spiking neural networks (SNNs) of the human brain.

Author dc:creator

• Farshadfar, Parsa

Advisor dc:contributor.advisor

• Kassiri, Hossein

• Electrical engineering

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

http://hdl.handle.net/10315/39067

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/39067