Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?
Abstract
:1. Introduction
1.1. TF Image Analysis in Epilepsy
1.2. Deep Learning for the Analysis of Visual Complexity
1.3. Our Contribution
2. Materials and Methods
2.1. Patient Cohort
2.2. iEEG Acquisition and Data Selection
2.3. Localization and Classification of Contacts
2.4. Epileptogenic Zone (EZ), Seizure Onset Zone (SOZ), and Post-Surgical Outcome
2.5. Time-Frequency Representation
2.6. Deep Learning-Based Visual Complexity
2.7. Identification of the “Points of Interest” Based on Their TF Image Visual Complexity
- (1)
- Patient- and layer-specific thresholding algorithm: thresholds were identified by fitting each layer’s UAE values to an extreme-value distribution [44,45,46]. The tail of the distribution contains the contacts with lower complexity, which we regarded as our points of interest in the brain. Extreme value theory pertains to the science of modeling and quantifying occurrences that have extremely low probabilities. Extreme value distribution can be generalized to distributions with the following probability density function (p) that depends on the location parameter (μ) and scale parameter (σ).
- (2)
- We used the UAE values from all the layers and passed them to a support vector machine’s classifier (see Figure 3b) with a linear kernel to classify each contact as a point of interest or not, where the ground truth label consisted of whether a point (contact) was inside or outside the EZ. As a validation strategy, we adopted a three-fold cross-validation, where we split the subjects into three distinct groups with similar class distribution [47] (similar proportion of contacts inside vs. outside the EZ). The number of contacts in the two classes (inside versus outside the EZ) for each group was: for Group 1, 259 inside versus 375 outside; for Group 2, 294 versus 378; and for Group 3, 244 versus 378. The groups were defined such that the subjects in each group were distinct (number of subjects per group: 7 in G1, 7 in G2, and 6 in G3).
2.8. Statistical Analysis
3. Results
3.1. UAE Values and Their Correspondance to the Seizure Onset Zone
3.2. Identification of the “Points of Interest”
4. Discussion
4.1. Comparison with Existing Methods
4.2. Considerations on the Automated Approach
4.3. Limitations and Future Directions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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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
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 StyleMakaram, 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