iEDeaL

Author:

Wang Qitong1,Whitmarsh Stephen2,Navarro Vincent2,Palpanas Themis3

Affiliation:

1. Université Paris Cité

2. Sorbonne Université

3. Université Paris Cité & French University Institute (IUF)

Abstract

Epilepsy is a chronic neurological disease, ranked as the second most burdensome neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is among the most important clinician operations to support epilepsy diagnosis, rendering automatic IED detection based on electroencephalography (EEG) signals an important topic. However, most existing solutions were designed and evaluated upon artificially balanced IED datasets, which do not conform to the real-world highly imbalanced scenarios. In this work, we propose the iEDeaL framework for automatic IED detection in challenging real-world use cases. The main components of iEDeaL are the new SC neural network architecture, to efficiently detect IEDs on raw EEG series instead of extracted features, and SaSu, a novel loss function to train SC by optimizing the F β -score. Experiments on two real-world imbalanced IED datasets verify the advantages of iEDeaL in offering more accurate and efficient IED detection when compared with other state-of-the-art deep learning-based and spectrogram feature-based solutions.

Publisher

Association for Computing Machinery (ACM)

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Reference105 articles.

1. Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG

2. Interictal epileptiform discharge characteristics underlying expert interrater agreement

3. Sara Bahaadini , Vahid Noroozi , Neda Rohani , Scott Coughlin , Michael Zevin , Joshua R. Smith , Vicky Kalogera , and Aggelos K . Katsaggelos . 2018 . Machine learning for Gravity Spy: Glitch classification and dataset. Information Sciences ( 2018). Sara Bahaadini, Vahid Noroozi, Neda Rohani, Scott Coughlin, Michael Zevin, Joshua R. Smith, Vicky Kalogera, and Aggelos K. Katsaggelos. 2018. Machine learning for Gravity Spy: Glitch classification and dataset. Information Sciences (2018).

4. Shaojie Bai , J. Zico Kolter , and Vladlen Koltun . 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv ( 2018 ). Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv (2018).

5. High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm

Cited by 7 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

同舟云学术

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