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Algorithms
Volume 16
Issue 12
10.3390/a16120567
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Article

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

by
Navaneethakrishna Makaram
1,
Sarvagya Gupta
1,
Matthew Pesce
1,
Jeffrey Bolton
2,
Scellig Stone
3,
Daniel Haehn
4,
Marc Pomplun
4,
Christos Papadelis
5,
Phillip Pearl
2,
Alexander Rotenberg
2,
Patricia Ellen Grant
1 and
Eleonora Tamilia
1,2,*
1
Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
2
Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
3
Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
4
Department of Computer Science, University of Massachusetts Boston, Boston, MA 02115, USA
5
Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
*
Author to whom correspondence should be addressed.
Algorithms 2023, 16(12), 567; https://doi.org/10.3390/a16120567
Submission received: 6 November 2023 / Revised: 8 December 2023 / Accepted: 13 December 2023 / Published: 15 December 2023
(This article belongs to the Special Issue Machine Learning Algorithms for Medical Image Processing)
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Abstract

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient’s MRI without requiring long-term iEEG inspection.
Keywords: epilepsy; deep learning; visual complexity; machine learning; image analysis; intracranial EEG; neurosurgery; MRI epilepsy; deep learning; visual complexity; machine learning; image analysis; intracranial EEG; neurosurgery; MRI

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MDPI and ACS Style

Makaram, N.; Gupta, S.; Pesce, M.; Bolton, J.; Stone, S.; Haehn, D.; Pomplun, M.; Papadelis, C.; Pearl, P.; Rotenberg, A.; et al. Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain? Algorithms 2023, 16, 567. https://doi.org/10.3390/a16120567

AMA Style

Makaram N, Gupta S, Pesce M, Bolton J, Stone S, Haehn D, Pomplun M, Papadelis C, Pearl P, Rotenberg A, et al. Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain? Algorithms. 2023; 16(12):567. https://doi.org/10.3390/a16120567

Chicago/Turabian Style

Makaram, Navaneethakrishna, Sarvagya Gupta, Matthew Pesce, Jeffrey Bolton, Scellig Stone, Daniel Haehn, Marc Pomplun, Christos Papadelis, Phillip Pearl, Alexander Rotenberg, and et al. 2023. "Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?" Algorithms 16, no. 12: 567. https://doi.org/10.3390/a16120567

APA Style

Makaram, N., Gupta, S., Pesce, M., Bolton, J., Stone, S., Haehn, D., Pomplun, M., Papadelis, C., Pearl, P., Rotenberg, A., Grant, P. E., & Tamilia, E. (2023). Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain? Algorithms, 16(12), 567. https://doi.org/10.3390/a16120567

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