Paroxysmal slow wave events predict epilepsy following a first seizure

Abstract

Objective: Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG-based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure.

Methods: We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow-up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs).

Results: Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%.

Significance: The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure.

Keywords: biomarker; epilepsy; first seizure; interictal EEG; new onset seizure; paroxysmal slow wave event.

FIGURE 1

Electroencephalogram (EEG) power spectral analysis. (A) The relative amplitude in the frequency bands δ (1–4 Hz), θ (4–8 Hz), α (8–12 Hz), β (12–20 Hz), low‐γ (20–30 Hz), and high‐γ (30–40 Hz) was calculated. Higher amplitude in the δ band and lower amplitude in the β (12–20Hz) and the low‐γ (20–30 Hz) were detected on interictal EEGs of the epilepsy group compared to both seizure‐free patients (p‐values: δ, .006; β, .031; low‐γ, .010) and controls (p‐values: δ, .013; β, .039; low‐γ, .008). No significant differences were observed in the θ, α, or high‐γ bands (p > .05). (B) Receiver operating characteristic (ROC) analysis for the relative amplitudes in the δ (area under the curve [AUC] = .69, p = .007), β (AUC = .65, p = .003), and low‐γ (AUC = .68, p = .01) bands revealed poor potential to serve as a biomarker as a single indicator for epilepsy. (C) The spectral changes observed between the seizure‐free and the epilepsy patients were not affected by administration of antiseizure medication (ASM; p > .05). (D) ROC analysis after excluding patients treated with ASMs compares the relative power in δ (AUC = .65, p = .027), β (AUC = .57, p = .043), and low‐γ (AUC = .68, p = .032). (E) The mean δ relative power in each of 19 electrodes for the seizure‐free group and epilepsy group. (F) Heat map demonstrates diffuse cortical slowing observed in epilepsy patients

FIGURE 2

Characterization of paroxysmal slow wave events (PSWEs) and the effect of antiseizure medication (ASM). (A) A representative PSWE detected in a segment of interictal electroencephalographic trace obtained for a patient with epilepsy following a first seizure in life is shown. (B) More PSWEs per minute were detected in patients with epilepsy (1.32 ± .18) compared to both controls (.57 ± .08) and seizure‐free patients (.7 ± .1; p < .001 and p = .001). (C) Receiver operating characteristic analysis testing the number of PSWEs per minute detected in patients with epilepsy compared to controls and seizure‐free patients combined (area under the curve = .72, p = .0008). (D) Following subgrouping according to ASMs use, no difference was observed within the seizure‐free patients (.786 ± .81 min⁻¹ vs. .577 ± .48 min⁻¹; p = .367) or within the patients with epilepsy (1.52 ± 1.10 min⁻¹ and ASM−: 1.09 ± .82 min⁻¹; p = .26). (E) Topographic heat maps show diffuse PSWEs in 19 scalp electrodes among seizure‐free patients versus epileptic patients. (F) Significant differences were observed in frontal, temporal, and parietal electrodes. MPF, median power frequency

FIGURE 3

Paroxysmal slow wave events (PSWEs) as a biomarker in epilepsy. (A) Median power frequency of PWEs in the epilepsy group and in the seizure‐free group. (B) Average duration of PSWEs in epilepsy and seizure‐free patients. (C) Fraction of time of the recorded electroencephalogram (EEG) showing PSWE activity. (D) Heat maps demonstrate the overall length of the PSWEs/time of recordings according to 19 scalp electrodes. (E) significant differences were observed in frontal, temporal, and parietal electrodes. (F) Timing of the EEG. Conducting an EEG within 3 days following a seizure increased the occurrence of PSWEs significantly (1.2 ± .13 vs. .4 ± .11; p < .001). (G) Occurrence per minute of PSWEs using only EEGs recorded close to the seizure event (the first 3 days). A significant difference between the seizure‐free group (.7 ± .1) and the epilepsy group (1.7 ± .2) was seen (p < .001). (H) Receiver operating characteristic analysis of seizure‐free patients and patients with epilepsy using EEG performed close to the seizure event (<3 days). * indicates significant difference (p < .05)