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In this study, we develop a novel fractal dimension-based testing approach that takes into account the dynamic topological properties of brain signals. By representing EEG brain signals as a sequence of Vietoris-Rips filtrations, our approach accommodates the inherent non-stationarities and irregularities of the signals. The application of our novel fractal dimension-based testing approach in analyzing dynamic topological patterns in EEG signals during an epileptic seizure episode exposes noteworthy alterations in total persistence across 0, 1, and 2-dimensional homology. These findings imply a more intricate influence of seizures on brain signals, extending beyond mere amplitude changes.<\/jats:p>","DOI":"10.3389\/fninf.2024.1387400","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T04:57:16Z","timestamp":1720760236000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals"],"prefix":"10.3389","volume":"18","author":[{"given":"Anass B.","family":"El-Yaagoubi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moo K.","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hernando","family":"Ombao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"B1","first-page":"1","article-title":"Persistence images: a stable vector representation of persistent homology","volume":"18","author":"Adams","year":"2017","journal-title":"J. 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