积极学习和边缘策略用于植入式设备中的心律失常分类
José-María Lillo-Castellano1, Inmaculada Mora-Jiménez1, María Martín-Méndez2
1Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Telematics and Computing Systems, Camino del Molino, 5. 28942, Fuenlabrada, Madrid, Spain.
Computers in biology and medicine
|February 14, 2025
概括
积极学习 (AL) 显著降低了从植入式心脏转移器除器 (ICD) 进行心律失常的手动标签负担. 这种人工智能方法可以用一小部分数据实现专家级分类性能.
科学领域:
- 心血管医学 心血管医学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 对心律失常发作的大量数据集可从植入式心脏转换器除器 (ICD) 中获得.
- 提取电生理学知识需要准确的插曲标签,这是目前由专家心脏病学家执行的手动,耗时和昂贵的过程.
研究的目的:
- 提出和评估积极学习 (AL) 策略,以简化心律不整期的手动标签.
- 开发分类模型,以减少大规模ICD数据集中的人类标签负担.
主要方法:
- 将四种基于大边际的积极学习 (AL) 策略调整为先前存在的分类方法.
- 基于3级和8级心律失常分类问题的基准AL策略,使用国家ICD数据库中的9908个发作.
- 评估了病例患者多样性对分类模型性能的影响.
主要成果:
- 在3类模型中,仅利用60%患者中约20%的发作,实现了黄金标准性能,相当于使用所有可用的发作.
- 对于8类模型,使用大约50%的发作来自85%的患者,可以达到专家水平的性能.
- 证明了AL在减少训练准确心律失常分类器所需数据方面的有效性.
结论:
- 积极学习 (AL) 技术在设计心律失常的分类模型方面具有显著的优势.
- AL有效地简化了广泛的植入式心脏转换器除器 (ICD) 数据集的人类标签过程.
- 这种方法有望加速从大型临床数据集中提取电生理学知识.
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