A novel approach based on BSPCI for quantifying functional connectivity pattern of the brain’s region for the classification of epileptic seizure
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-020-01774-w.pdf
Reference40 articles.
1. Alexandridis A (2013) Evolving RBF neural networks for adaptive soft-sensor design. Int J Neural Syst 23(06):1350029
2. Ashokkumar SR, MohanBabu G, Anupallavi S (2019) A KSOM based neural network model for classifying the epilepsy using adjustable analytic wavelet transform. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7359-0
3. Astolfi L et al (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28(2):143–157
4. Awal MA et al (2016) EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: a structured review. Clin Neurophysiol 127(1):285–296
5. Bandarabadi M et al (2015) Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 126(2):237–248
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Quantifying Functional Connectivity Network Through Synchronization and Graph Theory Approaches for Seizure Prediction;Wireless Personal Communications;2023-01-03
2. A novel peak signal feature segmentation process for epileptic seizure detection;International Journal of Information Technology;2020-10-12
3. Detection and diagnosis of brain tumors‐framework using extreme machine learning and CANFIS classification algorithms;International Journal of Imaging Systems and Technology;2020-09-04
4. Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier;Journal of Ambient Intelligence and Humanized Computing;2020-06-12
5. Effective epileptic seizure detection based on the event-driven processing and machine learning for mobile healthcare;Journal of Ambient Intelligence and Humanized Computing;2020-05-04
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3