作者
Omar Kaziha, Talal Bonny
发表日期
2020/2
研讨会论文
The 3rd IEEE International Conference on Engineering Innovations in Healthcare
简介
In this paper, a software-based neural network is developed for the purpose of detecting seizures from raw EEG signals. Detecting epileptic seizures manually is a long tedious process, creating the need for automatic detections systems. A neural network is designed based on a convolutional neural network (CNN) and trained on the electroencephalogram (EEG) raw signal dataset “CHB-MIT”. Deep learning has not been fully explored in seizure detection, but rather only classical machine learning algorithms that need feature extraction. There is a need to explore deep learning which eliminates manual feature extraction and enables real-time detection of raw signals. We conducted training and inference on the CHB-MIT dataset with a designed CNN in software and achieved an accuracy of 96.74%. The classifier can later be transformed into a portable system on chip (SoC) by realizing it on reconfigurable …
学术搜索中的文章
O Kaziha, T Bonny - 2020 Advances in Science and Engineering …, 2020