{
  "abstract": "Objective: Multi-label electrocardiogram (ECG) classification requires modeling morphological patterns across different temporal scales and their sequential relationships while maintaining limited model complexity. This study proposes Multi-Scale Channel-Attentive Mamba (MSCA-Mamba), a compact architecture integrating multi-scale feature extraction, branch-wise recalibration, and state-space sequence modeling. Methods: MSCA-Mamba combines three parallel dilated Conv1D branches, branch-wise Squeeze-and-Excitation modules, and a unidirectional Mamba block. The model was evaluated on the PTB-XL diagnostic superclass task using the official patient-wise split and five random seeds. Component ablations, parameter-matched Conv1D, GRU, LSTM, and Transformer controls, unidirectional-bidirectional comparisons, and GPU benchmarks were performed. Results: MSCA-Mamba achieved a Macro ROC-AUC of 0.8998 ± 0.0022, Macro PR-AUC of 0.7651 ± 0.0036, and Macro F1-score of 0.7038 ± 0.0045 with 121,149 parameters. Multi-scale feature extraction provided the clearest benefit, particularly for macro-averaged metrics. Parameter-matched alternatives showed broadly comparable performance. Bidirectional Mamba achieved a modestly higher Macro ROC-AUC but increased the parameter count to 214,077 and inference latency. Conclusions: MSCA-Mamba provides a compact framework for multi-label ECG classification on PTB-XL. Its primary strength lies in compact architectural integration rather than universal superiority of individual components.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Engineering, Malatya Turgut Ozal University, 44210 Malatya, Türkiye."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Dilber Cetintas",
      "orcid": "0000-0003-0710-2280"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/09tkn1ea0fgxm.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193135",
  "fulltext_url": "https://europepmc.org/articles/PMC13664229",
  "id": "09tkn1ea0fgxm",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882289",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2705|ECG Monitoring and Analysis"
    ]
  ],
  "openalex": "W7214666448",
  "pdf_url": "https://europepmc.org/articles/PMC13664229?pdf=render",
  "pmcid": "PMC13664229",
  "pmid": "42882289",
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/09tkn1ea0fgxm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882289",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882289"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|ECG Monitoring and Analysis",
      "subfield": "cardiovascular"
    }
  ],
  "title": "MSCA-Mamba: A Compact Multi-Scale Mamba Network for Multi-Label ECG Classification.",
  "title_text": "MSCA-Mamba: A Compact Multi-Scale Mamba Network for Multi-Label ECG Classification.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/09tkn1ea0fgxm",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}