Seizure detection in neonatal EEG signals using EMD based features

被引：0
|
作者：
Chandel, Garima [1 ,2 ]
Farooq, Omar [2 ]
Shaikh, Mohd Hamza Naim [3 ]
Shanir, Muhammad P. P. [2 ,4 ]
机构：
[1] ITS Engn Coll, Dept Elect & Commun Engn, Greater Noida, India
[2] AMU, Dept Elect Engn, Aligarh, Uttar Pradesh, India
[3] IIIT Delhi, Dept Elect & Commun Engn, Delhi 110020, India
[4] TKMCE, Dept Elect & Elect, Kollam, Kerala, India
来源：
PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT) | 2017年
关键词：
Electroencephalogram (EEG) signals; seizure detection; Empirical mode decomposition (EMD); EMPIRICAL MODE DECOMPOSITION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, an algorithm for epileptic seizure detection using empirical mode decomposition (EMD) method is proposed. The EMD technique decomposes EEG signals into intrinsic mode functions (IMF). Further, statistical features representing seizure and non-seizure EEG activities were computed over these IMFs. The useful features were selected using t-test score and fed to artificial neural network (ANN) classifier to recognizing seizure and non-seizure EEG. The scalp EEG signals recorded from neonatal subjects are used to test the efficacy of the proposed algorithm.
引用
收藏
页码：89 / 93
页数：5
相关论文
共 50 条
  • [1] EMD Analysis of EEG Signals for Seizure Detection
    Shaikh, Mohd Hamza Naim
    Farooq, Omar
    Chandel, Garima
    [J]. ADVANCES IN SYSTEM OPTIMIZATION AND CONTROL, 2019, 509 : 189 - 196
  • [2] LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM
    Zhang, Tao
    Chen, Wanzhong
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2017, 25 (08) : 1100 - 1108
  • [3] Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals
    Wulandari, Diah P.
    Putriz, Nomala G. P.
    Suprapto, Yoyon K.
    Purnami, Santi W.
    Juniani, Anda, I
    Islamiyah, Wardah R.
    [J]. FIFTH INFORMATION SYSTEMS INTERNATIONAL CONFERENCE, 2019, 161 : 568 - 576
  • [4] Classification of Epileptic Seizure EEG signals using EMD and ANFIS
    Pushpa, B.
    Najumnissa, D.
    [J]. 2014 INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV), 2014,
  • [5] Epileptic Seizure Prediction in EEG Signals using EMD and DWT
    Bekbalanova, Marzhan
    Zhunis, Aliya
    Duisebekov, Zhasdauren
    [J]. 2019 15TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTER AND COMPUTATION (ICECCO), 2019,
  • [6] Seizure Detection Exploiting EMD-Wavelet Analysis of EEG Signals
    Shahnaz, C.
    Raft, R. H. Md.
    Fattah, S. A.
    Zhu, W. -P.
    Ahmad, M. O.
    [J]. 2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2015, : 57 - 60
  • [7] A deep learning framework for epileptic seizure detection based on neonatal EEG signals
    Artur Gramacki
    Jarosław Gramacki
    [J]. Scientific Reports, 12
  • [8] A comparison of quantitative EEG features for neonatal seizure detection
    Greene, B. R.
    Faul, S.
    Marnane, W. P.
    Lightbody, G.
    Korotchikova, I.
    Boylan, G. B.
    [J]. CLINICAL NEUROPHYSIOLOGY, 2008, 119 (06) : 1248 - 1261
  • [9] A deep learning framework for epileptic seizure detection based on neonatal EEG signals
    Gramacki, Artur
    Gramacki, Jaroslaw
    [J]. SCIENTIFIC REPORTS, 2022, 12 (01)
  • [10] Classification of Normal, Seizure and Seizure-free EEG signals using EMD and EWT
    Saxena, Siddharth
    Hemanth, C.
    Sangeetha, R. G.
    [J]. 2017 INTERNATIONAL CONFERENCE ON NEXTGEN ELECTRONIC TECHNOLOGIES: SILICON TO SOFTWARE (ICNETS2), 2017, : 360 - 366
12345