TY - GEN
T1 - Seizure Onset Localization in Focal Epilepsy using intracranial-EEG data and the Schrodinger Operator's Spectrum
AU - Sadoun, Maria Sara Nour
AU - Laleg-Kirati, Taous Meriem
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that vary from short attention failure to convulsions. Despite its threats and limitations, existing medications target only specific types of seizures while up to 33% of epileptic conditions are drug-resistant. The best available treatment is surgical resection or neurostimulation and both require accurate localization of the Seizure Onset Zone. Its delineation is performed by analyzing neuronal activity by epileptologists, however, it is time-consuming and error-prone. Therefore, if the said zone could be located faster and more accurately, the seizure freedom of patients would be significantly enhanced. An effort within the field is aiming at developing computer-aided methods to assist medical experts and this starts with characterizing electrical neural activity. In the present paper, a new method for characterizing the epileptic intracranial EEG is proposed. The method is based on a semi-classical signal analysis (SCSA) method. Functional connectivity measures are used to compare patterns observed when feeding these measures with the raw time-series and when feeding them with SCSA features. The obtained results are undeniably promising for further investigation and improvement of the framework.Clinical relevance - The paper contributes to the design methods and algorithms to build reliable software solutions to assist medical experts in identifying Seizure Onset Zone in focal epilepsy.
AB - Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that vary from short attention failure to convulsions. Despite its threats and limitations, existing medications target only specific types of seizures while up to 33% of epileptic conditions are drug-resistant. The best available treatment is surgical resection or neurostimulation and both require accurate localization of the Seizure Onset Zone. Its delineation is performed by analyzing neuronal activity by epileptologists, however, it is time-consuming and error-prone. Therefore, if the said zone could be located faster and more accurately, the seizure freedom of patients would be significantly enhanced. An effort within the field is aiming at developing computer-aided methods to assist medical experts and this starts with characterizing electrical neural activity. In the present paper, a new method for characterizing the epileptic intracranial EEG is proposed. The method is based on a semi-classical signal analysis (SCSA) method. Functional connectivity measures are used to compare patterns observed when feeding these measures with the raw time-series and when feeding them with SCSA features. The obtained results are undeniably promising for further investigation and improvement of the framework.Clinical relevance - The paper contributes to the design methods and algorithms to build reliable software solutions to assist medical experts in identifying Seizure Onset Zone in focal epilepsy.
UR - http://www.scopus.com/inward/record.url?scp=85179642906&partnerID=8YFLogxK
U2 - 10.1109/EMBC40787.2023.10339987
DO - 10.1109/EMBC40787.2023.10339987
M3 - Conference contribution
C2 - 38083081
AN - SCOPUS:85179642906
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -