A complexity fingerprint for the localization of the Epileptogenic Zone using machine learning and data-driven approaches (2023)
ABSTRACT : In this research, we sought to delineate the epileptogenic zone using a dataset from the Cleveland Clinic, encompassing 28 patients who successfully underwent resective surgery and had prior SEEG recordings from both ictal and interictal periods. From time-windowed segments of these recordings, we derived complexity features and characterized them using their mean and standard deviation. Our analysis incorporated features such as Lempel-Ziv complexity, various entropies, fractal dimensions, and the 1/f slope of the brain activity spectrum, among others. We trained three distinct Logistic Regression Models: one using only ictal data, another using only interictal data, and a hybrid model leveraging both periods. Additionally, we trained a model using the Bern-Barcelona dataset, a known benchmark in interictal prediction. Our findings underscored that while the interictal period might be less informative in isolation, it enhances the insights drawn from the ictal phase when combined. A pivotal aspect of our research was discerning a distinctive epileptogenic zone fingerprint. Feature importance analysis pinpointed the Mean Lempel-Ziv Complexity, the standard deviation of the 1/f Slope, and the standard deviation of specific fractal dimensions as the most significant characteristics differentiating resected locations. These results not only contribute to understanding the epileptogenic zone but also foster discussions about complexity in the brain, particularly in the context of the brain criticality hypothesis.