TY - JOUR
KW - Clinical Feature
KW - Cloud Computing
KW - Deep Learning
KW - Edge Computing
KW - Electroencephalography
KW - Epilepsy
KW - Fuzzy
KW - Takagi-Sugeno-Kang (TSK)
AU - Shi Qiu
AU - Keyang Cheng
AU - Tao Zhou
AU - Rabia Tahir
AU - Liang Ting
AB - 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.
IS - Special Issue on Multimedia Streaming and Processing in Internet of Things with Edge Intelligence
M1 - 5
N2 - 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.
PY - 2022
SP - 6
EP - 13
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing
UR - https://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_1.pdf
VL - 7
SN - 1989-1660
ER -