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Purpose <p> Seizure recurrence, often presenting as clusters, is a major clinical concern linked to increased morbidity. The immediate postictal period is a critical yet understudied window where recurrence frequently arises. This study evaluates whether EEG features from postictal intervals can distinguish postictal-to-ictal (P–I) from postictal-to-interictal (P–Inter) transitions, enabling early recurrence prediction.</p> Methods <p> EEG data from the CHB-MIT database were analyzed, comprising 73 postictal episodes from seven patients (44 P–I, 29 P–Inter). Each episode was segmented into 10-second windows, yielding 876 segments. Fifty wavelet-based features were extracted from low-sample entropy channels and classified using Decision Tree (DT) and Long Short-Term Memory (LSTM) models. Performance was evaluated using nested cross-validation and, to test inter-patient generalization, per-patient stratified nested cross-validation.</p> Results <p> In subject-independent nested CV, DT achieved accuracy 0.75 (95% CI ± 0.029), sensitivity 0.73 (±0.041), specificity 0.77 (±0.038), F1-score 0.70 (±0.032), AUC 0.75 (±0.028), and FPR 0.20 (±0.039). LSTM yielded accuracy 0.71 (±0.027), sensitivity 0.69 (±0.066), specificity 0.72 (±0.069), F1-score 0.65 (±0.027), AUC 0.73 (±0.035), and FPR 0.28 (±0.069). Under patient-stratified evaluation, accuracy decreased to 0.67 (±0.076) for DT and 0.65 (±0.093) for LSTM, reflecting inter-patient variability.</p> Conclusion <p> These proof-of-concept findings indicate that postictal EEG, particularly P–I transitions, may encode information relevant to seizure recurrence. While the observed performance remains moderate, these results provide preliminary evidence warranting further investigation rather than indicating immediate clinical applicability.</p>

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Unlocking Insights from Postictal EEGs: Investigating Predictive Markers of Seizure Recurrence

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • Lyna Henaa Hasnaoui,
  • Abdelghani Djebbari

摘要

Purpose

Seizure recurrence, often presenting as clusters, is a major clinical concern linked to increased morbidity. The immediate postictal period is a critical yet understudied window where recurrence frequently arises. This study evaluates whether EEG features from postictal intervals can distinguish postictal-to-ictal (P–I) from postictal-to-interictal (P–Inter) transitions, enabling early recurrence prediction.

Methods

EEG data from the CHB-MIT database were analyzed, comprising 73 postictal episodes from seven patients (44 P–I, 29 P–Inter). Each episode was segmented into 10-second windows, yielding 876 segments. Fifty wavelet-based features were extracted from low-sample entropy channels and classified using Decision Tree (DT) and Long Short-Term Memory (LSTM) models. Performance was evaluated using nested cross-validation and, to test inter-patient generalization, per-patient stratified nested cross-validation.

Results

In subject-independent nested CV, DT achieved accuracy 0.75 (95% CI ± 0.029), sensitivity 0.73 (±0.041), specificity 0.77 (±0.038), F1-score 0.70 (±0.032), AUC 0.75 (±0.028), and FPR 0.20 (±0.039). LSTM yielded accuracy 0.71 (±0.027), sensitivity 0.69 (±0.066), specificity 0.72 (±0.069), F1-score 0.65 (±0.027), AUC 0.73 (±0.035), and FPR 0.28 (±0.069). Under patient-stratified evaluation, accuracy decreased to 0.67 (±0.076) for DT and 0.65 (±0.093) for LSTM, reflecting inter-patient variability.

Conclusion

These proof-of-concept findings indicate that postictal EEG, particularly P–I transitions, may encode information relevant to seizure recurrence. While the observed performance remains moderate, these results provide preliminary evidence warranting further investigation rather than indicating immediate clinical applicability.