EEG autoregressive modeling analysis: A diagnostic tool for patients with epilepsy without epileptiform discharges

Abstract

Objective: Numerous types of nonepileptic paroxysmal events, such as syncopes and psychogenic nonepileptic seizures, may imitate epileptic seizures and lead to diagnostic difficulty. Such misdiagnoses may lead to inappropriate treatment in patients that can considerably affect their lives. Electroencephalogram (EEG) is a commonly used tool in assisting diagnosis of epilepsy. Although the appearance of epileptiform discharges (EDs) in EEG recordings is specific for epilepsy diagnosis, only 25%-56% of patients with epilepsy show EDs in their first EEG examination.

Methods: In this study, we developed an autoregressive (AR) model prediction error-based EEG classification method to distinguish EEG signals between controls and patients with epilepsy without EDs. Twenty-three patients with generalized epilepsy without EDs in their EEG recordings and 23 age-matched controls were enrolled. Their EEG recordings were classified using AR model prediction error-based EEG features.

Results: Among different classification methods, XGBoost achieved the highest performance in terms of accuracy and true positive rate. The results showed that the accuracy, area under the curve, true positive rate, and true negative rate were 85.17%, 87.54%, 89.98%, and 81.81%, respectively.

Conclusions: Our proposed method can help neurologists in the early diagnosis of epilepsy in patients without EDs and might help in differentiating between nonepileptic paroxysmal events and epilepsy.

Significance: EEG AR model prediction errors could be used as an alternative diagnostic marker of epilepsy.

Keywords: Autoregressive model; EEG; Epilepsy; Epileptiform discharges; Nonepileptic paroxysmal events.