{
    "abstract": "The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowledge extraction. However, in this context, accurate and reliable episode labeling by expert cardiologists still remains a manual, costly, and time-consuming process.\nIn this work, we propose using Active Learning (AL) to design classification models that streamline the manual labeling of cardiac arrhythmic episodes. When AL is used, relevant episodes for classification are selected and then presented to the human expert for labeling, thereby dramatically reducing the manual labeling burden.\nWe adapted four large-margin-based AL strategies to a previously proposed classification methodology. We benchmarked them on problems involving 3 and 8 arrhythmia types using 9908 episodes from a massive national ICD data repository. Specifically, the relevance of episode-patient diversity for classification was evaluated. Results showed that the gold standard performance, achieved using all episodes, was reached by using approximately 20% (50%) of episodes from 60% (85%) of patients in the 3-class (8-class) model design.\nWe can conclude that AL techniques are advantageous for designing classification models and can streamline the human labeling process of large ICD datasets.",
    "authors": [
        {
            "affiliation": "Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Telematics and Computing Systems, Camino del Molino, 5. 28942, Fuenlabrada, Madrid, Spain.",
            "firstname": "Jos\u00e9-Mar\u00eda",
            "initials": "JM",
            "lastname": "Lillo-Castellano"
        },
        {
            "affiliation": "Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Telematics and Computing Systems, Camino del Molino, 5. 28942, Fuenlabrada, Madrid, Spain.",
            "firstname": "Inmaculada",
            "initials": "I",
            "lastname": "Mora-Jim\u00e9nez"
        },
        {
            "affiliation": "Medtronic Ib\u00e9rica \u00aeS.A, Dep. Cardiac Rhythm and Heart Failure, C/ Mar\u00eda de Portugal 9, 28050 Madrid, Spain.",
            "firstname": "Mar\u00eda",
            "initials": "M",
            "lastname": "Mart\u00edn-M\u00e9ndez"
        },
        {
            "affiliation": "Medtronic Ib\u00e9rica \u00aeS.A, Dep. Cardiac Rhythm and Heart Failure, C/ Mar\u00eda de Portugal 9, 28050 Madrid, Spain.",
            "firstname": "Laia",
            "initials": "L",
            "lastname": "Cerd\u00e1"
        },
        {
            "affiliation": "Hospital CU Virgen de la Arrixaca. Arrhythmia Unit, Ctra. Madrid-Cartagena, s/n. 30120-El Palmar Murcia, Spain.",
            "firstname": "Arcadi",
            "initials": "A",
            "lastname": "Garc\u00eda-Alberola"
        },
        {
            "affiliation": "Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Telematics and Computing Systems, Camino del Molino, 5. 28942, Fuenlabrada, Madrid, Spain; D!lemma Ltd startup, Camino del Molino, 5. 28942-Fuenlabrada Madrid, Spain. Electronic address: joseluis.rojo@urjc.es.",
            "firstname": "Jos\u00e9 Luis",
            "initials": "JL",
            "lastname": "Rojo-\u00c1lvarez"
        },
        {
            "affiliation": "Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, Environmental Computational Science and Earth Observation Laboratory, 1950-Sion, Switzerland.",
            "firstname": "Devis",
            "initials": "D",
            "lastname": "Tuia"
        }
    ],
    "conclusions": null,
    "copyrights": "Copyright \u00a9 2025 Elsevier Ltd. All rights reserved.",
    "doi": "10.1016/j.compbiomed.2025.109747",
    "journal": "Computers in biology and medicine",
    "keywords": [
        "Episode of cardiac arrhythmia",
        "Expert labeling",
        "Heterogeneous data",
        "Implantable cardioverter defibrillator",
        "Multi-signal",
        "Patient diversity",
        "Support vector machine"
    ],
    "methods": null,
    "publication_date": "2025-02-15",
    "pubmed_id": "39951979",
    "results": "We adapted four large-margin-based AL strategies to a previously proposed classification methodology. We benchmarked them on problems involving 3 and 8 arrhythmia types using 9908 episodes from a massive national ICD data repository. Specifically, the relevance of episode-patient diversity for classification was evaluated. Results showed that the gold standard performance, achieved using all episodes, was reached by using approximately 20% (50%) of episodes from 60% (85%) of patients in the 3-class (8-class) model design.",
    "title": "Active learning and margin strategies for arrhythmia classification in implantable devices.",
    "xml": "<Element 'PubmedArticle' at 0x788501fbbce0>"
}