{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusion: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pediatric Neurology and Respiratory Medicine, Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi City"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dan Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhengtao Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9akbx2svda3dm.bib",
  "cited_by": 0,
  "doi": "10.3389/fnins.2026.1884876",
  "fulltext_url": "https://europepmc.org/articles/PMC13714712",
  "id": "9akbx2svda3dm",
  "issns": [
    "1662-453X",
    "1662-4548"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1884876/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
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  "openalex": "W7221763235",
  "pdf_url": "https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1884876/pdf",
  "pmcid": "PMC13714712",
  "pmid": "42953147",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9akbx2svda3dm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:23Z",
      "native_id": "10.3389/fnins.2026.1884876",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fnins.2026.1884876/full"
    },
    {
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      "native_id": "MED:42953147",
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      "url": "https://europepmc.org/article/MED/42953147"
    },
    {
      "first_seen": "2026-11-01T12:22:09Z",
      "native_id": "PMC13714712",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fnins.2026.1884876"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "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",
  "title_text": "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",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:09Z",
  "url": "https://research.draconitic.com/paper/9akbx2svda3dm",
  "venue": "Frontiers in Neuroscience",
  "versions": []
}