BECT Spike Detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection-Reference-Cited by-同舟云学术

BECT Spike Detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection

Published:2022-04 Issue:4 Volume:69 Page:2366-2370
ISSN:1549-7747
Container-title:IEEE Transactions on Circuits and Systems II: Express Briefs
language:
Short-container-title:IEEE Trans. Circuits Syst. II

Author:

Wu Duanpo¹^ORCID,Shi Haichao¹^ORCID,Jiang Lurong²^ORCID,Dong Fang³,Liu Junbiao⁴,Cao Jiuwen⁵^ORCID,Jiang Tiejia⁶^ORCID,Wu Xunyi⁷

Affiliation:

1. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China

2. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China

3. College of Information and Electric Engineering, Zhejiang University City College, Hangzhou, China

4. Artistic Design and Creation School, Zhejiang University City College, Hangzhou, China

5. Machine Learning and I-Health International Cooperation Base of Zhejiang Province and the Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, China

6. Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

7. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China

Funder

Key Research and Development Programs in Zhejiang Province

National Key Research and Development Program

Joint Fund of Zhejiang Provincial Natural Science Foundation of China

NSFCZhejiang Integration Joint Fund

Open Research Projects of Zhejiang Lab

Open Project of Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8920/9741739/09714262.pdf?arnumber=9714262

Reference19 articles.

1. A study on fuzzy C-means clustering-based systems in automatic spike detection

2. Spike detection algorithm automatically adapted to individual patients applied to spike and wave percentage quantification

3. Empirical study of particle swarm optimization

4. Epilepsy Board Review

5. A Novel Spike Detection Algorithm Based on Multi-Channel of BECT EEG Signals

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