Search Advanced

Mathematical Biosciences and Engineering

2013, Volume 10, Issue 3: 579-590. doi: 10.3934/mbe.2013.10.579
Previous Article Next Article

Identifying preseizure state in intracranial EEG data using diffusion kernels

  • 1.
    101 AKW, 51 Prospect St. New Haven, CT 06511
  • 2.
    103 AKW, 51 Prospect St. New Haven, CT 06511
  • 3.
    716 LLCI, 15 York St. New Haven, CT 06520
  • 4.
    108A AKW, 51 Prospect St. New Haven, CT 06511
  • Received: 01 July 2012 Accepted: 29 June 2018 Published: 01 April 2013

  • MSC : 68T10, 65F15, 92B99, 92C20, 00A69.

  • The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of the data as well as pattern recognition that can be used to distinguish different states of the patient, for example, interictal and preseizure. A new algorithm, which is an extension of diffusion maps, is developed to construct coordinates that generate efficient geometric representations of the complex structures in the icEEG data. In addition, this method is adapted to the icEEG data and enables the extraction of the underlying brain activity.
       The algorithm is tested on icEEG data recorded from several electrode contacts from a patient being evaluated for possible epilepsy surgery at the Yale-New Haven Hospital. Numerical results show that the proposed approach provides a distinction between interictal and preseizure states.

    Citation: Dominique Duncan, Ronen Talmon, Hitten P. Zaveri, Ronald R. Coifman. Identifying preseizure state in intracranial EEG data using diffusion kernels[J]. Mathematical Biosciences and Engineering, 2013, 10(3): 579-590. doi: 10.3934/mbe.2013.10.579

    Related Papers:

    [1] N Arunkumar, B Nagaraj, M Ruth Keziah . EpilepIndex: A novel feature engineering tool to detect epilepsy using EEG signals. Mathematical Biosciences and Engineering, 2023, 20(12): 21670-21691. doi: 10.3934/mbe.2023959
    [2] Ning Huang, Zhengtao Xi, Yingying Jiao, Yudong Zhang, Zhuqing Jiao, Xiaona Li . Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals. Mathematical Biosciences and Engineering, 2024, 21(8): 6918-6935. doi: 10.3934/mbe.2024304
    [3] Bei Liu, Hongzi Bai, Wei Chen, Huaquan Chen, Zhen Zhang . Automatic detection method of epileptic seizures based on IRCMDE and PSO-SVM. Mathematical Biosciences and Engineering, 2023, 20(5): 9349-9363. doi: 10.3934/mbe.2023410
    [4] Dae Hyeon Kim, Jin-Oh Park, Dae-Young Lee, Young-Seok Choi . Multiscale distribution entropy analysis of short epileptic EEG signals. Mathematical Biosciences and Engineering, 2024, 21(4): 5556-5576. doi: 10.3934/mbe.2024245
    [5] Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul . Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism. Mathematical Biosciences and Engineering, 2025, 22(1): 73-105. doi: 10.3934/mbe.2025004
    [6] Enas Abdulhay, Maha Alafeef, Hikmat Hadoush, V. Venkataraman, N. Arunkumar . EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach. Mathematical Biosciences and Engineering, 2022, 19(5): 5031-5054. doi: 10.3934/mbe.2022235
    [7] Hongming Liu, Yunyuan Gao, Jianhai Zhang, Juanjuan Zhang . Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis. Mathematical Biosciences and Engineering, 2022, 19(1): 624-642. doi: 10.3934/mbe.2022028
    [8] Dominique Duncan, Thomas Strohmer . Classification of Alzheimer's disease using unsupervised diffusion component analysis. Mathematical Biosciences and Engineering, 2016, 13(6): 1119-1130. doi: 10.3934/mbe.2016033
    [9] Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh . Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition. Mathematical Biosciences and Engineering, 2021, 18(5): 5047-5068. doi: 10.3934/mbe.2021257
    [10] Xian Hua, Jing Li, Ting Wang, Junhong Wang, Shaojun Pi, Hangcheng Li, Xugang Xi . Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker. Mathematical Biosciences and Engineering, 2023, 20(6): 10530-10551. doi: 10.3934/mbe.2023465
  • The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of the data as well as pattern recognition that can be used to distinguish different states of the patient, for example, interictal and preseizure. A new algorithm, which is an extension of diffusion maps, is developed to construct coordinates that generate efficient geometric representations of the complex structures in the icEEG data. In addition, this method is adapted to the icEEG data and enables the extraction of the underlying brain activity.
       The algorithm is tested on icEEG data recorded from several electrode contacts from a patient being evaluated for possible epilepsy surgery at the Yale-New Haven Hospital. Numerical results show that the proposed approach provides a distinction between interictal and preseizure states.


    [1] Phys. Rev. E, 67 (2003), 10901.
    [2] NeuroImage, 52 (2010), 1162-1170.
    [3] Appl. Comp. Harm. Anal., 21 (2006), 5-30.
    [4] Challenges of Modern Technology, 1 (2010), 27-29.
    [5] Epilepsy and Behavior, 19 (2010), 4-16.
    [6] Epilepsia, 50 (2009), 2575-85.
    [7] Mayo. Clin. Proc., 71 (1996), 576-586.
    [8] Appl. Comp. Harm. Anal., 32 (2012), 280-294.
    [9] N. Engl. J. Med., 342 (2000), 314-319.
    [10] Brain, 130 (2007), 314-333.
    [11] in "Medical Image Computing and Computer-Assisted Intervention" (Eds. C. Barillot, D. Haynor and P. Hellier) Saint-Malo: Springer, (2004), 763-770.
    [12] IEEE Trans. Signal Process, ASSP-28, 55-69.
    [13] Elsevier, 22 (2011), S88-S93.
    [14] Appl. Comp. Harm. Anal., 25 (2008), 226-239.
    [15] Neurology Today, 9 (2009), 22-23.
    [16] submitted to PNAS, (2012).
    [17] IEEE Trans. Signal Process, 60 (2012).

  • This article has been cited by:

    1. Dominique Duncan, Paul Vespa, Arthur W. Toga, Detecting features of epileptogenesis in EEG after TBI using unsupervised diffusion component analysis, 2018, 23, 1553-524X, 161, 10.3934/dcdsb.2018010
    2. Ronen Talmon, Stephane Mallat, Hitten Zaveri, Ronald R. Coifman, Manifold Learning for Latent Variable Inference in Dynamical Systems, 2015, 63, 1053-587X, 3843, 10.1109/TSP.2015.2432731
    3. Ariel Schwartz, Ronen Talmon, Intrinsic Isometric Manifold Learning with Application to Localization, 2019, 12, 1936-4954, 1347, 10.1137/18M1198752
    4. Ioannis K. Gallos, Evangelos Galaris, Constantinos I. Siettos, Construction of embedded fMRI resting-state functional connectivity networks using manifold learning, 2020, 1871-4080, 10.1007/s11571-020-09645-y
    5. N. Rabin, D. Fishelov, Multi-scale kernels for Nyström based extension schemes, 2018, 319, 00963003, 165, 10.1016/j.amc.2017.02.025
    6. Maria A. Veretennikova, Alla Sikorskii, Michael J. Boivin, Parameters of stochastic models for electroencephalogram data as biomarkers for child’s neurodevelopment after cerebral malaria, 2018, 5, 2195-5832, 10.1186/s40488-018-0086-7
    7. M. Z. Ahmad, Maryam Saeed, Sajid Saleem, Awais M. Kamboh, 2016, Seizure detection using EEG: A survey of different techniques, 978-1-5090-3552-6, 1, 10.1109/ICET.2016.7813209
    8. Wenzhao Lian, Ronen Talmon, Hitten Zaveri, Lawrence Carin, Ronald Coifman, Multivariate time-series analysis and diffusion maps, 2015, 116, 01651684, 13, 10.1016/j.sigpro.2015.04.003
    9. Dominique Duncan, Thomas Strohmer, Classification of Alzheimer's disease using unsupervised diffusion component analysis, 2016, 13, 1551-0018, 1119, 10.3934/mbe.2016033
    10. Neta Rabin, Maayan Kahlon, Sarit Malayev, Anat Ratnovsky, Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques, 2020, 149, 09574174, 113281, 10.1016/j.eswa.2020.113281
    11. Dominique Duncan, Giuseppe Barisano, Ryan Cabeen, Farshid Sepehrband, Rachael Garner, Adebayo Braimah, Paul Vespa, Asla Pitkänen, Meng Law, Arthur W. Toga, Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients, 2018, 12, 1662-5196, 10.3389/fninf.2018.00086
    12. Evangelos Almpanis, Constantinos Siettos, Construction of functional brain connectivity from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach, 2020, 7, 2373-7972, 66, 10.3934/Neuroscience.2020005
    13. Hau-tieng Wu, Ronen Talmon, Yu-Lun Lo, Assess Sleep Stage by Modern Signal Processing Techniques, 2015, 62, 0018-9294, 1159, 10.1109/TBME.2014.2375292
    14. Constantinos Siettos, Jens Starke, Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools, 2016, 8, 19395094, 438, 10.1002/wsbm.1348
    15. Dominique Duncan, Paul Vespa, Asla Pitkänen, Adebayo Braimah, Niina Lapinlampi, Arthur W. Toga, Big data sharing and analysis to advance research in post-traumatic epilepsy, 2019, 123, 09699961, 127, 10.1016/j.nbd.2018.05.026
    16. Haidar Khan, Lara Marcuse, Madeline Fields, Kalina Swann, Bulent Yener, Focal Onset Seizure Prediction Using Convolutional Networks, 2018, 65, 0018-9294, 2109, 10.1109/TBME.2017.2785401
  • Reader Comments
  • © 2013 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Mathematical Biosciences and Engineering
4.4

Metrics

Article views(3280) PDF downloads(513) Cited by(16)

Preview PDF
Download XML
Export Citation
Article outline
Abstract
References

Other Articles By Authors

Related pages

Tools

Copyright © AIMS Press

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog