{
  "abstract": "There is a growing need to automate EEG monitoring of preterm-born infants, particularly where expert evaluation is limited. However, existing models necessitate human intervention for comprehensive assessment, thereby limiting the clinical applicability. We collected 165 EEG records from 65 preterm-born infants. 8-channel EEG records were segmented every 30 s and allocated into eight classes (three preterm-equivalent, four term-equivalent, and one noise class). We performed noise detection, sleep state classification, and maturity assessment through the classification. ImageNet pretrained ResNet-50 was finetuned to classify the segmented EEG images. A novel index, named “maturity rate,” was calculated as the proportion of term-equivalent labels in each EEG recording. The model achieved a macro F1 score of 0.58 and Cohen’s Kappa of 0.58 for the 8-class classification. When the labels were merged into active sleep, quiet sleep, and noise categories, performance improved to an F1 score of 0.84 and Kappa of 0.74. The maturity rate increased continuously with postmenstrual age at the time of recording, reflecting functional brain development beyond the term–preterm dichotomy. Our model enables end-to-end EEG assessment of sleeping newborns by simultaneously performing noise detection, sleep state classification, and maturity assessment, with minimal need for human intervention.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Education, The University of Tokyo, Bunkyo, Tokyo, Japan; Division of Neural Dynamics, National Institute for Physiological Sciences, Okazaki, Aichi, Japan; Correspondence to: Division of Neural Dynamics, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji-cho, Okazaki-shi, Aichi 444-8585, Japan.",
    "Graduate School of Education, The University of Tokyo, Bunkyo, Tokyo, Japan",
    "Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan",
    "Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan",
    "Division of Neonatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Aichi, Japan",
    "Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan; Department of Developmental Disability Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ryo Fujihira"
    },
    {
      "affs": [
        1
      ],
      "name": "Hama Watanabe"
    },
    {
      "affs": [
        2
      ],
      "name": "Anna Shiraki"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuji Ito"
    },
    {
      "affs": [
        3
      ],
      "name": "Takafumi Ushida"
    },
    {
      "affs": [
        2
      ],
      "name": "Hiroyuki Yamamoto"
    },
    {
      "affs": [
        2
      ],
      "name": "Tomohiko Nakata"
    },
    {
      "affs": [
        4
      ],
      "name": "Yoshiaki Sato"
    },
    {
      "affs": [
        5
      ],
      "name": "Jun Natsume"
    },
    {
      "affs": [
        2
      ],
      "name": "Hiroyuki Kidokoro"
    },
    {
      "affs": [
        1
      ],
      "name": "Gentaro Taga"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1dcj2b7zsdjvv.bib",
  "cited_by": 0,
  "doi": "10.1016/j.neures.2026.105139",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S0168010226001264",
  "id": "1dcj2b7zsdjvv",
  "issns": [
    "0168-0102",
    "1872-8111"
  ],
  "landing_url": "https://doi.org/10.1016/j.neures.2026.105139",
  "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",
      "RC321-571"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7222586266",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/1dcj2b7zsdjvv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:33Z",
      "native_id": "a11773e94bc3465181b2c154fc2374d2",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.neures.2026.105139"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "Automation in sleep state classification and maturity evaluation of EEG in preterm-born infants",
  "title_text": "Automation in sleep state classification and maturity evaluation of EEG in preterm-born infants",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:33Z",
  "url": "https://research.draconitic.com/paper/1dcj2b7zsdjvv",
  "venue": "Neuroscience Research",
  "versions": []
}