Seizure Onset Localization in Focal Epilepsy using intracranial-EEG data and the Schrodinger Operator's Spectrum.

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.

EEG Epileptic Data Classification Using the Schrodinger Operator's Spectrum.

Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable. A new research tendency when tackling epileptic seizure detection tends towards minimizing human manual intervention by designing frameworks with autonomous feature engineering. In this optic, this paper proposes a new approach for EEG epileptic data classification. Features derived from the Semi-Classical Signal Analysis (SCSA) method, a quantum-inspired signal processing method well-suited for the characterization of pulse-shaped physiological signals, are proposed. In addition nonlinear dynamical features that proved efficient in characterizing nonlinear dynamics of neural activity have been extracted. Moreover, hyperparameters' optimization, correlation analysis and feature selection have been performed. The selected features are fed into five different machine learning classifiers. The performance of the proposed approach has been analyzed using Bonn university database. The results show that all classifiers yield a performance accuracy of 93% and above.Clinical relevance- The paper contributes to the design of methods and algorithms to build reliable software solutions to assist medical experts and reduce epilepsy disease's diagnosis time and errors.