Abstract
The contemporary use of interictal scalp electroencephalography (EEG) in the context of focal epilepsy workup relies on the visual identification of interictal epileptiform discharges. The high-specificity performance of this marker comes, however, at a cost of only moderate sensitivity. Zero-crossing interval analysis is an alternative to Fourier analysis for the assessment of the rhythmic component of EEG signals. We applied this method to standard EEG recordings of 78 patients divided into 4 subgroups: temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), psychogenic nonepileptic seizures (PNES) and nonepileptic patients with headache. Interval-analysis based markers were capable of effectively discriminating patients with epilepsy from those in control subgroups (AUC~0.8) with diagnostic sensitivity potentially exceeding that of visual analysis. The identified putative epilepsy-specific markers were sensitive to the properties of the alpha rhythm and displayed weak or non-significant dependences on the number of antiepileptic drugs (AEDs) taken by the patients. Significant AED-related effects were concentrated in the theta interval range and an associated marker allowed for identification of patients on AED polytherapy (AUC~0.9). Interval analysis may thus, in perspective, increase the diagnostic yield of interictal scalp EEG. Our findings point to the possible existence of alpha rhythm abnormalities in patients with epilepsy.
Highlights
Since the advent of electroencephalography, changes in the alpha rhythm have been associated with epilepsy3
Interval analysis has complex mathematical properties and is closely related to nonlinear signal analysis methods16. It has recently emerged once again as a useful tool for data compression in nonlinear signal analysis and was successfully applied to human scalp EEG data17,18. We demonstrate that this partially forgotten tool, when used for alpha rhythm assessment, provides putative new epilepsy-specific markers which may, in perspective, be used to improve the diagnostic yield of standard scalp EEG recordings
We retrospectively studied EEG recordings from 78 patients (20 male and 58 female, aged 18–68 years, mean 35 years) who were hospitalized in the Departament of Neurology and Epileptology, Medical Centre for Postgraduate Education, Warsaw, Poland during the years 2009–2013
Summary
Since the advent of electroencephalography, changes in the alpha rhythm have been associated with epilepsy. More recently a series of automated, computer-based methods have been tested for assessment of background EEG activity, and have shown slowing of the alpha rhythm in patients with epilepsy. There have been a few attempts to construct a complete computer-assisted diagnosis (CAD) tool for epilepsy based on the analysis of the interictal scalp EEG www.nature.com/scientificreports/ Both in adult and paediatric populations12 – providing some optimistic results despite their complex methodologies (including the use of artificial neural networks, ANN). Interval analysis has complex mathematical properties and is closely related to nonlinear signal analysis methods16 It has recently emerged once again as a useful tool for data compression in nonlinear signal analysis and was successfully applied to human scalp EEG data. We demonstrate that this partially forgotten tool, when used for alpha rhythm assessment, provides putative new epilepsy-specific markers which may, in perspective, be used to improve the diagnostic yield of standard scalp EEG recordings
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