<bold>A Time-Frequency Based Method for the Detection of Epileptic Seizures in EEG Recordings</bold>

被引：16
|
作者：
Tzallas, Alexandros T. [1 ]
Tsipouras, Markos G. [2 ]
Fotiadis, Dirnitrios I. [2 ]
机构：
[1] Univ Ioannina, Dept Med Phys, Sch Med, GR-45110 Ioannina, Greece
[2] Univ Ioannina, Dept Comp Sci, Unit Med Technol & Intelligent Informat Syst, GR-45110 Ioannina, Greece
来源：
TWENTIETH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/CBMS.2007.17
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel three-stage method for the analysis of electroencephalographic (EEG) signals, concerning epileptic seizures, is proposed First, segments of the EEG signals are analyzed using a time-frequency distribution and then, several features are extracted for each segment, representing the energy distribution over the time-frequency plane. Those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments (existence of epileptic seizure or not). The evaluation results are very promising, indicating overall accuracy from 89.4% to 99%.
引用
收藏
页码：135 / +
页数：2
相关论文
共 50 条
  • [21] Using damping time for epileptic seizures detection in EEG
    Li, X
    Guan, X
    Du, R
    MODELLING AND CONTROL IN BIOMEDICAL SYSTEMS 2003 (INCLUDING BIOLOGICAL SYSTEMS), 2003, : 255 - 258
  • [22] 7 tesla interleaved EEG and fMRI recordings: BOLD measurements
    Bonmassar, G
    Van De Moortele, PF
    Purdon, P
    Jaaskelainen, I
    Ives, JR
    Vaughan, TJ
    Uguribil, K
    Belliveau, JW
    NEUROIMAGE, 2001, 13 (06) : S4 - S4
  • [23] A High Q 2D-Length Extension Mode Resonator for potential Time-Frequency applications<bold> </bold>
    Chapellier, P.
    Lavenus, P.
    Dulmet, B.
    2018 SYMPOSIUM ON DESIGN, TEST, INTEGRATION & PACKAGING OF MEMS AND MOEMS (DTIP), 2018,
  • [24] Window-based Time-Frequency Methods for Analyzing Epileptic EEG Signals
    Yan, Yi
    Samdin, S. Balqis
    Minhad, Khairun Nisa'
    2022 IEEE-EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES, IECBES, 2022, : 292 - 297
  • [25] Epileptic Seizure Detection Based on Video and EEG Recordings
    Aghaei, Hoda
    Kiani, Mohammad Mandi
    Aghajan, Hamid
    2017 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS), 2017,
  • [26] Novel quadratic time-frequency features in EEG signals for robust detection of epileptic seizure
    Ghembaza F.
    Djebbari A.
    Research on Biomedical Engineering, 2023, 39 (02) : 365 - 387
  • [27] Epileptic Seizure Detection with Hybrid Time-Frequency EEG Input: A Deep Learning Approach
    Pan, Yayan
    Zhou, Xiaoyu
    Dong, Fanying
    Wu, Jianxiang
    Xu, Yongan
    Zheng, Shilian
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2022, 2022
  • [28] Transfer function between EEG and BOLD signals of epileptic activity
    Leite, Marco
    Leal, Alberto
    Figueiredo, Patricia
    FRONTIERS IN NEUROLOGY, 2013, 4
  • [29] Anticipation of epileptic seizures from standard EEG recordings
    De Clercq, W
    Lemmerling, P
    Van Huffel, S
    Van Paesschen, W
    LANCET, 2003, 361 (9361): : 970 - 970
  • [30] Anticipation of epileptic seizures from standard EEG recordings
    Le Van Quyen, M
    Martinerie, J
    Navarro, V
    Boon, P
    D'Havé, M
    Adam, C
    Renault, B
    Varela, F
    Baulac, M
    LANCET, 2001, 357 (9251): : 183 - 188
12345