Classification of Epileptic and Psychogenic Non-Epileptic Seizures Using Electroencephalography and Electrocardiography

Abstract

Patients with psychogenic non-epileptic seizures (PNES) may exhibit similar clinical features to patients with epileptic seizures (ES). Misdiagnosis of PNES and ES can lead to inappropriate treatment and significant morbidity. This study investigates the use of machine learning techniques for classification of PNES and ES based on electroencephalography (EEG) and electrocardiography (ECG) data. Video-EEG-ECG of 150 ES events from 16 patients and 96 PNES from 10 patients were analysed. Four preictal periods (time before event onset) in EEG and ECG data were selected for each PNES and ES event (60-45 min, 45–30 min, 30–15 min, 15–0 min). Time-domain features were extracted from each preictal data segment in 17 EEG channels and 1 ECG channel. The classification performance using k-nearest neighbour, decision tree, random forest, naive Bayes, and support vector machine classifiers were evaluated. The results showed the highest classification accuracy was 87.83% using the random forest on 15–0 min preictal period of EEG and ECG data. The performance was significantly higher using 15–0 min preictal period data than 30–15 min, 45–30 min, and 60–45 min preictal periods ( $\text{p} < 0.001$ ). The classification accuracy was improved from 86.37% to 87.83% by combining ECG data with EEG data ( $\text{p} < 0.001$ ). The study provided an automated classification algorithm for PNES and ES events using machine learning techniques on preictal EEG and ECG data.

Keywords

• Classification
• epileptic seizures (ES)
• electroencephalography (EEG)
• electrocardiography (ECG)
• machine learning
• psychogenic non-epileptic seizures (PNES)