A representation-level wavelet–EMD fusion framework for spectral analysis and detection of epileptiform patterns in pediatric EEG: computational validation with a roadmap for external benchmarking
- 1Department of Pediatric Neurology and Respiratory Medicine, Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi City
Abstract
Background: Pediatric EEG analysis for epilepsy detection is challenged by the non-stationary and developmentally heterogeneous nature of epileptiform activity. Conventional Fourier approaches provide limited time–frequency localization, whereas single-domain methods such as the wavelet transform and empirical mode decomposition (EMD) each exhibit complementary weaknesses.
Methods: We developed a representation-level wavelet–EMD fusion framework that integrates wavelet and EMD spectra before feature extraction. The pipeline included adaptive wavelet denoising, energy-criterion EMD, weighted time–frequency fusion, extraction of eight spectral–temporal features, and classification using a soft-voting ensemble of a radial basis function support vector machine, random forest, and neural network. Evaluation was performed on 2,000 synthetic pediatric EEG segments spanning canonical epileptiform morphologies and multiple signal-to-noise ratios. A feature-level fusion variant was implemented as an ablation comparator to isolate the effect of fusion strategy while preserving identical feature extraction, classifier architecture, and validation procedures.
Results: Across 10-fold stratified cross-validation with 100 Monte Carlo replications, the proposed framework achieved 94.6 ± 0.6% accuracy, 93.7 ± 0.8% sensitivity, 95.5 ± 0.5% specificity, F1 score of 0.943 ± 0.007, and AUROC of 0.971 (95% CI, 0.962–0.978). Performance exceeded standalone wavelet, EMD, and Fourier baselines (all p < 0.001). Representation-level fusion also outperformed feature-level fusion by 1.4 percentage points in accuracy, with the greatest gains observed for polyspike and slow-wave morphologies. On the synthetic corpus the framework was competitive with EEGNet and a 1D convolutional network while retaining explicit time–frequency interpretability. In an external feasibility validation on the CHB-MIT pediatric scalp EEG corpus (14 subjects, leave-one-subject-out), representation-level fusion attained the highest performance of all methods (AUROC 0.901, accuracy 86.7%), confirming that the fusion strategy transfers to real recordings, with performance decreasing as expected.
Conclusion: Representation-level wavelet–EMD fusion provides an interpretable and transparent computational framework for analyzing non-stationary pediatric epileptiform EEG whose advantage over classical time–frequency baselines persists, at feasibility scale, on real CHB-MIT recordings and remains competitive with deep-learning baselines. Because the primary evaluation is synthetic and the external validation is a feasibility-scale demonstration, these figures should be read as evidence of methodological viability and external transportability rather than definitive clinical performance; validation on larger, multi-center and prospective pediatric cohorts is the essential next step.
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BibTeX
@article{feng2026representationlevel,
title = {{A representation-level wavelet–EMD fusion framework for spectral analysis and detection of epileptiform patterns in pediatric EEG: computational validation with a roadmap for external benchmarking}},
author = {Dan Feng and Zhengtao Li},
journal = {Frontiers in Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnins.2026.1884876},
url = {https://www.frontiersin.org/articles/10.3389/fnins.2026.1884876/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A representation-level wavelet–EMD fusion framework for spectral analysis and detection of epileptiform patterns in pediatric EEG: computational validation with a roadmap for external benchmarking
AU - Dan Feng
AU - Zhengtao Li
PY - 2026
DA - 2026/10/14
JO - Frontiers in Neuroscience
DO - 10.3389/fnins.2026.1884876
UR - https://www.frontiersin.org/articles/10.3389/fnins.2026.1884876/full
AB - Background: Pediatric EEG analysis for epilepsy detection is challenged by the non-stationary and developmentally heterogeneous nature of epileptiform activity. Conventional Fourier approaches provide limited time–frequency localization, whereas single-domain methods such as the wavelet transform and empirical mode decomposition (EMD) each exhibit complementary weaknesses. Methods: We developed a representation-level wavelet–EMD fusion framework that integrates wavelet and EMD spectra before feature extraction. The pipeline included adaptive wavelet denoising, energy-criterion EMD, weighted time–frequency fusion, extraction of eight spectral–temporal features, and classification using a soft-voting ensemble of a radial basis function support vector machine, random forest, and neural network. Evaluation was performed on 2,000 synthetic pediatric EEG segments spanning canonical epileptiform morphologies and multiple signal-to-noise ratios. A feature-level fusion variant was implemented as an ablation comparator to isolate the effect of fusion strategy while preserving identical feature extraction, classifier architecture, and validation procedures. Results: Across 10-fold stratified cross-validation with 100 Monte Carlo replications, the proposed framework achieved 94.6 ± 0.6% accuracy, 93.7 ± 0.8% sensitivity, 95.5 ± 0.5% specificity, F1 score of 0.943 ± 0.007, and AUROC of 0.971 (95% CI, 0.962–0.978). Performance exceeded standalone wavelet, EMD, and Fourier baselines (all p < 0.001). Representation-level fusion also outperformed feature-level fusion by 1.4 percentage points in accuracy, with the greatest gains observed for polyspike and slow-wave morphologies. On the synthetic corpus the framework was competitive with EEGNet and a 1D convolutional network while retaining explicit time–frequency interpretability. In an external feasibility validation on the CHB-MIT pediatric scalp EEG corpus (14 subjects, leave-one-subject-out), representation-level fusion attained the highest performance of all methods (AUROC 0.901, accuracy 86.7%), confirming that the fusion strategy transfers to real recordings, with performance decreasing as expected. Conclusion: Representation-level wavelet–EMD fusion provides an interpretable and transparent computational framework for analyzing non-stationary pediatric epileptiform EEG whose advantage over classical time–frequency baselines persists, at feasibility scale, on real CHB-MIT recordings and remains competitive with deep-learning baselines. Because the primary evaluation is synthetic and the external validation is a feasibility-scale demonstration, these figures should be read as evidence of methodological viability and external transportability rather than definitive clinical performance; validation on larger, multi-center and prospective pediatric cohorts is the essential next step.
SN - 1662-453X
SN - 1662-4548
ER -