01954nas a2200301   4500000000100000000000100001008004100002260001200043653002100055653002000076653001800096653001900114653002700133653001300160653001000173653002900183100001200212700001700224700001300241700001600254700001500270245010100285856008000386300000900466490000600475520115700481022001401638       2022                            d  c09/202210aClinical Feature10aCloud Computing10aDeep Learning10aEdge Computing10aElectroencephalography10aEpilepsy10aFuzzy10aTakagi-Sugeno-Kang (TSK)1 aShi Qiu1 aKeyang Cheng1 aTao Zhou1 aRabia Tahir1 aLiang Ting00aAn EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing  uhttps://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_1.pdf  a6-130 v73 aEpilepsy 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.  a1989-1660