Neonatal seizure monitoring using non-linear EEG analysis

被引:34
|
作者
Smit, LS
Vermeulen, RJ
Fetter, WPF
Strijers, RLM
Stam, CJ
机构
[1] Free Univ Amsterdam, Med Ctr, Dept Clin Neurophysiol, MEG Ctr, NL-1007 MB Amsterdam, Netherlands
[2] Free Univ Amsterdam, Med Ctr, Dept Child Neurol, NL-1007 MB Amsterdam, Netherlands
[3] Free Univ Amsterdam, Med Ctr, Dept Neonatol, NL-1007 MB Amsterdam, Netherlands
关键词
neonatal; hypoxia-ischaemia; seizure; EEG; detection synchronization; non-linear;
D O I
10.1055/s-2004-830367
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Birth asphyxia is a major concern in neonatal care. Epileptic seizures are associated with subsequent neurodevelopmental deficits. Eighty-five percent of these seizures remain subclinical and therefore an on-line monitoring device is needed. In an earlier study we showed that the synchronization likelihood was able to distinguish between neonatal EEG epochs with and without epileptic seizures. In this study we investigated whether the synchronization likelihood can be used in complete EEGs, without artifact removal. Twenty complete EEGs from 20 neonatal patients were studied. The synchronization likelihood was calculated and correlated with the visual scoring done by 3 experts. In addition, we determined the influence of seizure length on the likelihood of detection. Using the raw unfiltered EEG data we found a sensitivity of 65.9% and a specificity of 89.8% for the detection of seizure activity in each epoch. In addition, the seizure detection rate was 100% when the seizures lasted for 100 seconds or more. The synchronization likelihood seems to be a useful tool in the automatic monitoring of epileptic seizures in infants on the neonatal ward. Due to the retrospective nature of our study, the consequences for clinical intervention cannot yet be determined and prospective studies are needed. Therefore, we will conduct a prospective study on the neonatal intensive care unit with a recently developed on-line version of the synchronization likelihood analysis.
引用
收藏
页码:329 / 335
页数:7
相关论文
共 50 条
  • [1] Seizure anticipation by non-linear EEG analysis
    Van Quyen, ML
    [J]. EPILEPTIC DISORDERS, 2001, 3 : s79 - s89
  • [2] Non-linear classifiers applied to EEG analysis for epilepsy seizure detection
    Martinez-Del-Rincon, Jesus
    Santofimia, Maria J.
    del Toro, Xavier
    Barba, Jesus
    Romero, Francisca
    Navas, Patricia
    Lopez, Juan C.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2017, 86 : 99 - 112
  • [3] Linear and non-linear measures of the human neonatal EEG
    Hecox, K
    Nayak, S
    Gin, K
    McGee, A
    van Drongelen, W
    [J]. NEUROCOMPUTING, 2003, 52-4 : 779 - 786
  • [4] Linear and non-linear measures of human neonatal EEG
    Hecox, K.
    Nayak, S.
    Gin, K.
    McGee, A.
    van Drongelen, W.
    [J]. Neurocomputing, 2003, 52-54 : 779 - 786
  • [5] Epileptic Seizure Detection Using Non Linear Analysis of EEG
    Vijith, V. S.
    Jacob, Jisu Elsa
    Iype, Thomas
    Gopakumar, K.
    Yohannan, Doris George
    [J]. 2016 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), VOL 3, 2015, : 225 - 230
  • [6] Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA
    Shanmugam, Shalini
    Dharmar, Selvathi
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 131
  • [7] Non-linear analysis of the sleep EEG
    Kobayashi, T
    Misaki, K
    Nakagawa, H
    Madokoro, S
    Ihara, H
    Tsuda, K
    Umezawa, Y
    Murayama, J
    Isaki, K
    [J]. PSYCHIATRY AND CLINICAL NEUROSCIENCES, 1999, 53 (02) : 159 - 161
  • [8] Usefulness of non-linear EEG analysis
    Micheloyannis, S
    Flitzanis, N
    Papanikolaou, E
    Bourkas, M
    Terzakis, D
    Arvanitis, S
    Stam, CJ
    [J]. ACTA NEUROLOGICA SCANDINAVICA, 1998, 97 (01): : 13 - 19
  • [9] DETECTION OF EPILEPTIC SEIZURE IN CHILDREN USING NON-LINEAR ANALYSIS IS HRV
    Kharytonov, V.
    Chaikovsky, I.
    Bukhman, V.
    Frolov, Y.
    Mishiev, V.
    [J]. EPILEPSIA, 2012, 53 : 238 - 238
  • [10] Automated seizure detection using limited-channel EEG and non-linear dimension reduction
    Birjandtalab, Javad
    Pouyan, Maziyar Baran
    Cogan, Diana
    Nourani, Mehrdad
    Harvey, Jay
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 82 : 49 - 58