An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing

Author
Keywords
Abstract
Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure.
Year of Publication
2022
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
7
Issue
Special Issue on Multimedia Streaming and Processing in Internet of Things with Edge Intelligence
Number
5
Number of Pages
6-13
Date Published
09/2022
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_1.pdf
DOI
10.9781/ijimai.2022.07.001
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