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...

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Veröffentlicht in:	IEEE transactions on biomedical circuits and systems Jg. 19; H. 4; S. 756 - 766
Hauptverfasser:	Kim, Bokyung, Huang, Qijia, Taylor, Brady, Zheng, Qilin, Ku, Jonathan, Chen, Yiran, Li, Hai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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