MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM

Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures req...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on biomedical circuits and systems Vol. 19; no. 4; pp. 756 - 766
Main Authors:	Kim, Bokyung, Huang, Qijia, Taylor, Brady, Zheng, Qilin, Ku, Jonathan, Chen, Yiran, Li, Hai
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Artificial neural networks Biological system modeling Brain modeling Chips (memory devices) Classification Classification algorithms Co-design Computation Computer architecture computing-in-memory Convulsions & seizures cross-patient diagnosis automation EEG Electroencephalography Epilepsy Epilepsy - classification Epilepsy - diagnosis Fast Fourier transforms Hardware Humans input-stationary dataflow Multiclass seizure Neural networks Neural Networks, Computer neural processor patient-independence Quality of life Quantization (signal) seizure classification seizure detection seizure prediction Seizures Seizures - classification Seizures - diagnosis Signal Processing, Computer-Assisted SRAM-CIM Static random access memory Training
ISSN:	1932-4545, 1940-9990, 1940-9990
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!