Time series entropies and chaos theory to understand children epileptic encephalopathies

The goal of this project is to develop time series entropy measures and applying chaos theory to understand (and possibly) predict seizures in children epileptic encephalopathies. These are characterized by being drug resistant, described by multiple massive long term-EEG. As a result of this, neurologists may find it difficult to give objective opinions about the evolution of the little patient considering the historic bunch of EEG. This complicates more if we want to identify (objectively as well) the more affected area of the brain through all the patient’s life. Moreover, how could we compare quantitatively a given patient with another one’s progress? How to measure objectively (taking into account all the above mentioned circumstances) if the AED (antiepilpetic drugs) are working efficiently? We have already answered these questions in diverse papers of ours (and others still submitted or being currently developed), but we still need to solve the problem of finding clinical markers and prediction.

Start date: 11 July 2015 | Proposed end date: 22 July 2025
Region: Latin America
Languages: English, Spanish, German
Lead institution: Instituto Tecnologico de Monterrey, Campus Ciudad de Mexico
Location: Mexico City, Mexico
Principal investigator / organizer: Dr. Ricardo Zavala-Yoe
Patient age: Pediatric, Adult
Type of project: Clinical, Research
Funding sources: Government, Institutional
Project needs: Funding, Hospitals that can provide EEG data in Doose/Lennox-Gastaut syndromes
Related publications:

  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A.. (2022). Simultaneous Evaluation of Children Epileptic Encephalopathies with Long-Term EEG via Space-Time Dynamic Entropies. 10.1201/9781003145240-4.
  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A.. (2019). Dynamische Entropie-Trajektorien zum gleichzeitigen Vergleich von Patienten mit Doose und Lennox-Gastaut Syndrome ESCI-Journal. Zeitschrift für Epileptologie. 32.
  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A.. (2018). Retrospektive inter-und intra-patientale Evaluation von Epileptischen Enzephalopathien durch synchronisierten Vergleich von dynamischen Komplexitätsmaßen des langzeit EEG.
  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A.. (2017). Dynamic Complexity Measures and Entropy Paths for Modelling and Comparison of Evolution of Patients with Drug Resistant Epileptic Encephalopathy Syndromes (DREES). Metabolic Brain Disease. 32. doi:10.1007/s11011-017-0036-y.
  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A. & Morales-Menendez, Ruben. (2017). Real Time Acquisition and Processing of Massive Electro- Encephalographic Signals for Modeling by Nonlinear Statistics ESCI-Journal. International Journal for Interactive Design and Manufacturing (IJIDeM). 10.1007/s12008-016-0366-8.
  • Zavala-Yoe R, Ramirez-Mendoza RA, Cordero LM. Entropy measures to study and model long term simultaneous evolution of children in Doose and Lennox-Gastaut syndromes. J Integr Neurosci. 2016 Jun;15(2):205-21. doi: 10.1142/S0219635216500138.
  • Zavala-Yoé R, Ramírez-Mendoza R, Cordero LM. Novel way to investigate evolution of children refractory epilepsy by complexity metrics in massive information. Springerplus. 2015 Aug 21;4:437. doi: 10.1186/s40064-015-1173-6.
  • Zavala-Yoe, Ricardo & Ramirez-Mendoza, Ricardo A. & Jimenez-Botello, Luis. (2015). Mathematical Complexity As Alternative to Deal with Multiple Massive Data EEG in Children Epilepsy ESCI-Journal. Zeitschrift für Epileptologie. 28. 12.

Contact: Dr. Ricardo Zavala-Yoe,Tec Mty