A multiresolution approach to spike detection in EEG

被引：0
|
作者：
Calvagno, G [1 ]
Ermani, M [1 ]
Rinaldo, R [1 ]
Sartoretto, F [1 ]
机构：
[1] Univ Padua, Dipartimento Elettron & Informat, I-35131 Padua, Italy
来源：
2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS, VOLS I-VI | 2000年
关键词：
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
A technique is proposed for the automatic detection of spikes in electroencephalograms (EEG). A multiresolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three subbands using a non-decimated wavelet transform. Each subband is analyzed by using a non-linear energy operator, in order to detect, peaks. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three Subbands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts.
引用
收藏
页码：3582 / 3585
页数：4
相关论文
共 50 条
  • [31] EEG spike detection and source reconstruction using a protocol language
    Wagner, M
    Fuchs, M
    Kastner, J
    EPILEPSIA, 1999, 40 : 198 - 198
  • [32] Interictal Spike Detection in EEG using Time Series Classification
    Sablok, Shlok
    Gururaj, Githali
    Shaikh, Naushaba
    Shiksha, I
    Choudhary, Antara Roy
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020), 2020, : 644 - 647
  • [33] Epileptic Spike Detection with EEG using Artificial Neural Networks
    Carey, Howard J., III
    Manic, Milos
    Arsenovic, Paul
    2016 9TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTIONS (HSI), 2016, : 89 - 95
  • [34] Intracranial EEG spike detection based on rhythm information and SVM
    Yang, Baoshan
    Hu, Yegang
    Zhu, Yu
    Wang, Yuping
    Zhang, Jicong
    2017 NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC 2017), VOL 2, 2017, : 382 - 385
  • [35] Model-Based Spike Detection of Epileptic EEG Data
    Liu, Yung-Chun
    Lin, Chou-Ching K.
    Tsai, Jing-Jane
    Sun, Yung-Nien
    SENSORS, 2013, 13 (09) : 12536 - 12547
  • [36] EEG spike detection using time-frequency signal analysis
    Hassanpour, H
    Mesbah, M
    Boashash, B
    2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL V, PROCEEDINGS: DESIGN AND IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS INDUSTRY TECHNOLOGY TRACKS MACHINE LEARNING FOR SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING SIGNAL PROCESSING FOR EDUCATION, 2004, : 421 - 424
  • [37] AUTOMATIC DETECTION OF SPIKE-AND-WAVE BURSTS IN AMBULATORY EEG RECORDINGS
    KOFFLER, DJ
    GOTMAN, J
    ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1985, 61 (02): : 165 - 180
  • [38] Adaptive segmentation of EEG based on a dipole model for epileptic spike detection
    Van Hese, P
    Hallez, H
    Claeys, P
    Vonck, K
    Lemahieu, I
    Van de Walle, R
    Boon, P
    EPILEPSIA, 2004, 45 : 176 - 176
  • [39] Detection of the EEG spike-wave patterns evoked by volatile anaesthetics
    Olejarczyk, E.
    Rudner, R.
    Marciniak, R.
    Wartak, M.
    Stasiowski, Michal
    Jalowiecki, Przemyslaw
    Sobieszek, A.
    WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, VOL 25, PT 4: IMAGE PROCESSING, BIOSIGNAL PROCESSING, MODELLING AND SIMULATION, BIOMECHANICS, 2010, 25 : 407 - 409
  • [40] AUTOMATED INTERICTAL EEG SPIKE DETECTION USING ARTIFICIAL NEURAL NETWORKS
    GABOR, AJ
    SEYAL, M
    ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1992, 83 (05): : 271 - 280
12345