{
  "abstract": "To address the heavy reliance of deep learning models on manually labeled data in ECG classification, we need to tackle two major limitations. First, sample sizes are often limited in clinical settings, making data annotation costly and impractical. Second, existing few-shot learning methods still fall short in two key areas: 1) Existing strategies pay insufficient attention to rare arrhythmia types. This makes it difficult to effectively identify rare clinical cases. 2) Single feature representation fails to fully integrate the global frequency, local time-frequency, and semantic information of ECG signals. This results in incomplete feature representation. To overcome these challenges, we propose a novel few-shot learning method for ECG classification. In terms of feature extraction, a spectral-temporal convolution feature extractor is designed to precisely focus on the key information of ECG by efficiently integrating the global frequency, local time-frequency features and semantic information of the signal. Furthermore, with regard to classification, an improved prototypical network based on cross-class bias diminishing is developed to enhance the recognition performance for rare arrhythmia type samples. The experimental results show that the proposed method achieves significant classification performance across multiple public datasets, especially on Chapman, it attains an average accuracy exceeding 90% in the 2-way 1-shot and 2-way 5-shot tasks.",
  "abstract_shown": true,
  "affiliations": [
    "School of Electronics and Information Engineering, Wuyi University, Jiangmen, China",
    "School of Mathematics and Computational Science, Wuyi University, Jiangmen, China",
    "Department of Cardiovascular Disease and Clinical Experimental Center, Jiangmen Central Hospital, Jiangmen, China",
    "Department of Intensive Care Unit, Jiangmen Central Hospital, Jiangmen, China",
    "Department of Electrocardiogram, Jiangmen Central Hospital, Jiangmen, China",
    "Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jia Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Dainan Gan"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaolin Zhan"
    },
    {
      "affs": [
        3
      ],
      "name": "Wei Yao"
    },
    {
      "affs": [
        4
      ],
      "name": "Bin Zou"
    },
    {
      "affs": [
        5
      ],
      "name": "Bin Zhang"
    },
    {
      "name": "Dong Li"
    },
    {
      "name": "Dan Li"
    },
    {
      "name": "Lin Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/50haa0mg1dqyw.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357954",
  "fulltext_url": null,
  "id": "50haa0mg1dqyw",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0357954",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2705|ECG Monitoring and Analysis"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Engineering and technology/Technology development/Prototypes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Arrhythmia"
    ],
    [
      "plos",
      "/Medicine and health sciences/Cardiology/Heart rate/Tachycardia"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Cardiac electrophysiology/Electrocardiography"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Social sciences/Linguistics/Semantics"
    ]
  ],
  "openalex": "W7222107102",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357954&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/50haa0mg1dqyw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0357954",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357954"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cardiology",
      "subfield": "cardiovascular"
    }
  ],
  "title": "STCP-Net: A few-shot learning approach based on spectral-temporal convolution and an improved prototypical network for ECG classification",
  "title_text": "STCP-Net: A few-shot learning approach based on spectral-temporal convolution and an improved prototypical network for ECG classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/50haa0mg1dqyw",
  "venue": "PLOS One",
  "versions": []
}