Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy.

Won-Du Chang,Hoon-Chul Kang,Chang-Hwan Im,Chany Lee,Ho-Seung Cha

doi:10.1155/2016/8701973

Abstract

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

Highlights

Epileptiform discharges (EDs) are signal patterns frequently observed in interictal electroencephalogram (EEG) of patients with epilepsy [1]
Epileptiform sharps are detected using a variety of algorithms such as deterministic finite automata (DFA) [3], geometrical features and artificial neural networks [4], cross-correlation [5], and dynamic time warping [6]
The number of studies to design algorithms for SSW detection is relatively less than that for sharps detection, a variety of approaches have been used for the automatic detection of SSWs, such as Fourier transforms [8], wavelet transforms [9,10,11], Gotman and Gloor’s spike detection method [12], a rulebased system [13], and a detection method using a number of geometrical features [14]

Summary

Introduction

Epileptiform discharges (EDs) are signal patterns frequently observed in interictal electroencephalogram (EEG) of patients with epilepsy [1]. We propose a new method based on a novel wave detection algorithm for the detection and identification of different types of EDs. The proposed method was evaluated with intracranial EEG (iEEG) data recorded from a patient with Lennox-Gastaut syndrome (LGS), a type of secondary generalized epilepsy in which the patient suffers from multiple seizure types, generating a variety of EDs on iEEG [22]. Among the various interictal EDs, we have focused on repeated sharps and runs of SSWs, as these forms are hard to detect with existing algorithms These characteristic EEG patterns have great potential to aid in surgical planning of patients with secondary generalized epilepsy. The performance of our proposed algorithm was evaluated and compared with that of conventional frequency domain approaches

Materials and Method

B Signal

Results and Discussion

Conclusion