STMemAE: An Instance-Level Based Spatio-Temporal Memory Autoencoder for Unsupervised Vision-Based Seizure Detection

Electroencephalogram (EEG) is most favorable in epilepsy analysis, but suffered from inconvenient recording and ease in disturbation. Contrastively, the vision-based seizure detection is more feasible in real applications for 24/7 monitoring. However, most vision-based seizure detections follow the...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 5; pp. 3298 - 3310
Main Authors:	Hu, Dinghan, Wu, Kai, Fang, Yuan, Jiang, Tiejia, Gao, Feng, Cao, Jiuwen
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Annotations Brain modeling Computer vision Convulsions & seizures Electroencephalography Epilepsy Feature extraction gradient loss Image reconstruction instance-level Machine learning Memory modules Motion perception Object recognition Prototypes Seizures STMemAE Training Unsupervised learning unsupervised seizure detection Visualization YOLO
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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