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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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 9; H. 5; S. 3298 - 3310
Hauptverfasser:	Hu, Dinghan, Wu, Kai, Fang, Yuan, Jiang, Tiejia, Gao, Feng, Cao, Jiuwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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