Pierpaolo Croce1, Lorenzo Ricci2, Patrizia Pulitano3, Marilisa Boscarino4, Filippo Zappasodi5, Jacopo Lanzone6, Flavia Narducci4, Oriano Mecarelli3, Vincenzo Di Lazzaro4, Mario Tombini4, Giovanni Assenza4. 1. Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy. 2. Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy. Electronic address: lorenzo.ricci@unicampus.it. 3. Department of Neurology and Psychiatry, "Sapienza" University of Rome, Italy. 4. Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy. 5. Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy. 6. Department of Systems Medicine, Neuroscience, University of Rome Tor Vergata, Rome, Italy; Neurorehabilitation Department, IRCCS Salvatore Maugeri Foundation, Institute of Milan, Milan, Italy.
Abstract
OBJECTIVE: To determine the predictive power for seizure-freedom of 19-channels EEG, measured both before and after three months the initiation of the use of Levetiracetam (LEV), in a cohort of people after a new diagnosis of temporal-lobe epilepsy (TLE) using a machine-learning approach. METHODS: Twenty-three individuals with TLE were examined. We dichotomized clinical outcome into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG effective power in different frequency bands was compared using baseline EEG (T0) and the EEG after three months of LEV therapy (T1) between SF and NSF patients. Partial Least Square (PLS) analysis was used to test and validate the prediction of the model for clinical outcome. RESULTS: A total of 152 features were extracted from the EEG recordings. When considering only the features calculated at T1, a predictive power for seizure-freedom (AUC = 0.750) was obtained. When employing both T0 and T1 features, an AUC = 0.800 was obtained. CONCLUSIONS: This study provides a proof-of-concept pipeline for predicting the clinical response to anti-seizure medications in people with epilepsy. SIGNIFICANCE: Future studies may benefit from the pipeline proposed in this study in order to develop a model that can match each patient to the most effective anti-seizure medication.
OBJECTIVE: To determine the predictive power for seizure-freedom of 19-channels EEG, measured both before and after three months the initiation of the use of Levetiracetam (LEV), in a cohort of people after a new diagnosis of temporal-lobe epilepsy (TLE) using a machine-learning approach. METHODS: Twenty-three individuals with TLE were examined. We dichotomized clinical outcome into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG effective power in different frequency bands was compared using baseline EEG (T0) and the EEG after three months of LEV therapy (T1) between SF and NSF patients. Partial Least Square (PLS) analysis was used to test and validate the prediction of the model for clinical outcome. RESULTS: A total of 152 features were extracted from the EEG recordings. When considering only the features calculated at T1, a predictive power for seizure-freedom (AUC = 0.750) was obtained. When employing both T0 and T1 features, an AUC = 0.800 was obtained. CONCLUSIONS: This study provides a proof-of-concept pipeline for predicting the clinical response to anti-seizure medications in people with epilepsy. SIGNIFICANCE: Future studies may benefit from the pipeline proposed in this study in order to develop a model that can match each patient to the most effective anti-seizure medication.