A reliable data-driven pipeline based on deep learning is introduced to differentiate between individuals with epileptic seizures (ES), psychogenic non-epileptic seizures (PNES), and control subjects (CS) using non-invasive, low-density interictal scalp EEG recordings. The study recruited 42 subjects with ES (new onset), 42 subjects with PNES diagnosed via video-EEG, and 19 CS with normal EEG. Subjects taking psychotropic drugs were excluded to avoid alterations in the EEG signal. The proposed methodology involves automatically extracting features from the 19-channel EEG channels using Empirical Mode Decomposition (EMD) and a customized Convolutional Neural Network (CNN) with a convolutional processing module, rectified linear units (ReLu), and pooling layer to extract and learn relevant features and perform the necessary classification. The CNN displayed excellent classification performance, achieving an accuracy of 85.7%, thereby fostering the use of deep processing systems to aid physicians in challenging clinical situations.
Lo Giudice, M., Mammone, N., Ieracitano, C., Aguglia, U., Mandic, D., Morabito, F.C. (2023). A Convolutional Neural Network Approach for the Classification of Subjects with Epileptic Seizures Versus Psychogenic Non-epileptic Seizures and Control, Based on Automatic Feature Extraction from Empirical Mode Decomposition of Interictal EEG Recordings. In Smart Innovation, Systems and Technologies (pp. 207-214). Springer Science and Business Media Deutschland GmbH [10.1007/978-981-99-3592-5_20].
A Convolutional Neural Network Approach for the Classification of Subjects with Epileptic Seizures Versus Psychogenic Non-epileptic Seizures and Control, Based on Automatic Feature Extraction from Empirical Mode Decomposition of Interictal EEG Recordings
Lo Giudice M.
Writing – Original Draft Preparation
;
2023-01-01
Abstract
A reliable data-driven pipeline based on deep learning is introduced to differentiate between individuals with epileptic seizures (ES), psychogenic non-epileptic seizures (PNES), and control subjects (CS) using non-invasive, low-density interictal scalp EEG recordings. The study recruited 42 subjects with ES (new onset), 42 subjects with PNES diagnosed via video-EEG, and 19 CS with normal EEG. Subjects taking psychotropic drugs were excluded to avoid alterations in the EEG signal. The proposed methodology involves automatically extracting features from the 19-channel EEG channels using Empirical Mode Decomposition (EMD) and a customized Convolutional Neural Network (CNN) with a convolutional processing module, rectified linear units (ReLu), and pooling layer to extract and learn relevant features and perform the necessary classification. The CNN displayed excellent classification performance, achieving an accuracy of 85.7%, thereby fostering the use of deep processing systems to aid physicians in challenging clinical situations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
