Differentiation between Epileptic and Psychogenic Nonepileptic Seizures in Electroencephalogram Using Wavelets and Support-Vector Machines

• Published in: Applied artificial intelligence Vol. 36; no. 1
• Main Authors: dos Santos, Kauê Reis, de Abreu de Sousa, Miguel Angelo, dos Santos, Sara Dereste, Pires, Ricardo, Thome-Souza, Sigride
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accelerometers; Convulsions & seizures; Discrete Wavelet Transform; Electroencephalography; Epilepsy; Kernels; Seizures; Support vector machines; Wavelet transforms
• ISSN: 0883-9514; 1087-6545
• DOI: 10.1080/08839514.2021.2008612

The number of people suffering from epilepsy in the world exceeds 50 million and around 20% of this group refers to patients who have psychogenic nonepileptic seizures (PNES). Unlike epilepsy, PNES requires psychological treatment. When not correctly diagnosed, these patients can be submitted to a treatment based on antiepileptic drugs besides specific procedures for epilepsy. In this work, we propose the identification of patients with PNES from those with epilepsy using electroencephalogram (EEG) signals. Discrete Wavelet Transform (DWT) decomposition and a Support-Vector Machine (SVM) classifier were employed. Common types of wavelet families and SVM kernels were combined and compared. The results obtained for accuracy, sensitivity, and specificity are equal to 100% for the set of configuration parameters composed of windows encompassing whole seizures, wavelet Coiflet 1, and SVM kernel sigmoid or RBF. The proposed method is efficient and feasible to be applied to new patients admitted in a hospital center, even without having their previous EEG signals already collected. The main advantages of the proposed work are not requiring the use of accelerometer nor electromyographic signals, not being patient specific and outperforming other works results.