{
  "abstract": "For pediatric patients with respiratory insufficiency, long-term non-invasive ventilation (LT-NIV) represents an essential treatment. Despite significant technological progress, the commercial built-in ventilator software has structural limitations: its automatic scoring algorithms are typically calibrated for adult respiratory patterns, failing to detect the high respiratory rate variability inherent to children. As a result, clinicians must perform a manual data review to identify episodes of obstructive sleep apnea. While Machine Learning (ML) has demonstrated significant potential in identifying these events, most studies focus on adults and employ multi-channel polysomnography data. Addressing this, we investigated automated methods for sleep apnea detection using only a single-channel respiratory signal from built-in ventilator software. Therefore, we propose a novel hybrid approach combining a Bidirectional Long Short-Term Memory (BiLSTM) network with a Feature Engineering-based ML ensemble. Experimental results, validated through a Leave-One-Out strategy, demonstrate that the hybrid model can identify sleep apnea events despite the high variability in pediatric respiratory rates and the significant class imbalance within the dataset. The findings provide encouraging evidence for the potential use of the proposed approach as a decision-support tool for automated analysis of respiratory signals in pediatric LT-NIV. However, prospective validation on larger and more diverse pediatric cohorts will be necessary before clinical deployment.",
  "abstract_shown": true,
  "affiliations": [
    "Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Friuli-Venezia Giulia, Italy; Corresponding author.",
    "Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, Via dell’Istria, 65, Trieste, 34137, Italy",
    "Department of spinal surgery and Pain managment Krankenhaus Neuwer, Dünner Str. 214-216, Mönchengladbach, 41066, Germany",
    "Institute for Maternal and Child Health-IRCCS “Burlo Garofolo”, Via dell’Istria, 65, Trieste, 34137, Italy; Department of Medical, Surgical and Health Sciences, University of Trieste, Strada di Fiume, 447, 34129, Trieste, 34129, Italy",
    "Mathematics Area, mathLab, SISSA, Via Bonomea, 265, Trieste, 34136, Friuli-Venezia Giulia, Italy"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Laura Meneghetti"
    },
    {
      "affs": [
        1
      ],
      "name": "Sergio Ghirardo"
    },
    {
      "affs": [
        2
      ],
      "name": "Alberto Favero"
    },
    {
      "affs": [
        1
      ],
      "name": "Francesca Policastro"
    },
    {
      "affs": [
        3
      ],
      "name": "Alessandro Amaddeo"
    },
    {
      "affs": [
        4
      ],
      "name": "Gianluigi Rozza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6es170ybfzz46.bib",
  "cited_by": 0,
  "doi": "10.1016/j.cmpbup.2026.100273",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2666990026000431",
  "id": "6es170ybfzz46",
  "issns": [
    "2666-9900"
  ],
  "landing_url": "https://doi.org/10.1016/j.cmpbup.2026.100273",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "2737|Obstructive Sleep Apnea Research"
    ]
  ],
  "openalex": "W7223200910",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S2666990026000431/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/6es170ybfzz46.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:51Z",
      "native_id": "080fc1dea3834096ae3f80c3b138870c",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.cmpbup.2026.100273"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2737|Obstructive Sleep Apnea Research",
      "subfield": "physiology"
    }
  ],
  "title": "Sleep apnea detection in pediatric LT-NIV via a hybrid LSTM-machine learning framework",
  "title_text": "Sleep apnea detection in pediatric LT-NIV via a hybrid LSTM-machine learning framework",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:26Z",
  "url": "https://research.draconitic.com/paper/6es170ybfzz46",
  "venue": "Computer Methods and Programs in Biomedicine Update",
  "versions": []
}