Tampere University of Technology

TUTCRIS Research Portal

Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines

Research output: Contribution to journalArticleScientificpeer-review

Standard

Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines. / Zabihi, Morteza; Kiranyaz, Serkan; Jäntti, Ville; Lipping, Tarmo; Gabbouj, Moncef.

In: IEEE Journal of Biomedical and Health Informatics, 2019.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

APA

Vancouver

Author

Zabihi, Morteza ; Kiranyaz, Serkan ; Jäntti, Ville ; Lipping, Tarmo ; Gabbouj, Moncef. / Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines. In: IEEE Journal of Biomedical and Health Informatics. 2019.

Bibtex - Download

@article{5d3af41e0e5946338c5313ed2882d168,
title = "Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines",
abstract = "Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in 3D exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25{\%} of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15{\%} average sensitivity and 95.16{\%} average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.",
author = "Morteza Zabihi and Serkan Kiranyaz and Ville J{\"a}ntti and Tarmo Lipping and Moncef Gabbouj",
note = "EXT={"}Kiranyaz, Serkan{"}",
year = "2019",
doi = "10.1109/JBHI.2019.2906400",
language = "English",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines

AU - Zabihi, Morteza

AU - Kiranyaz, Serkan

AU - Jäntti, Ville

AU - Lipping, Tarmo

AU - Gabbouj, Moncef

N1 - EXT="Kiranyaz, Serkan"

PY - 2019

Y1 - 2019

N2 - Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in 3D exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25% of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15% average sensitivity and 95.16% average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.

AB - Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in 3D exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25% of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15% average sensitivity and 95.16% average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.

U2 - 10.1109/JBHI.2019.2906400

DO - 10.1109/JBHI.2019.2906400

M3 - Article

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

ER -