使用转移学习架构检测心律失常,集成开发的优化算法和规范化方法
Fatma Akalın1, Pınar Dervişoğlu Çavdaroğlu2, Mehmet Fatih Orhan3
1Department of Information Systems Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya, Turkey. fatmaakalin@sakarya.edu.tr.
BMC biomedical engineering
|June 30, 2025
概括
这项研究引入了一种增强的MobileNetv2模型用于儿科心电图 (ECG) 分析,提高了检测心律异常的准确性和稳定性. 优化的模型在复杂的数据集上展示了可靠的性能.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 对于诊断心律异常至关重要,但可能耗时且具有挑战性,特别是在儿科患者中,由于独特的ECG模式.
- 现有的心电图分析方法虽然有效,但往往取决于临床医生的专业知识,并且可能缺乏跨不同数据集的概括性.
研究的目的:
- 开发和验证一个优化的深度学习模型,用于准确和稳定的儿科心电图数据的分类.
- 通过整合新的优化和规范化技术,提高儿科患者心电图分析模型的通用性.
主要方法:
- 创建了一个定制的儿科心电图数据集,包括1318个异常和1403个正常节拍.
- 移动Netv2传输学习架构得到了提议优化算法V5和提议规范化方法V5的增强.
- 该模型在平衡的2类数据集和更复杂的6类数据集上进行了评估.
主要成果:
- 增强的MobileNetv2模型在2类数据集上实现了0.9801和0.9509的训练和测试准确度,超过了原始架构 (0.9633和0.9399).
- 在6个类别的数据集上,该模型实现了0.9200%和0.8975%的训练和测试准确度,证明了可接受的性能和通用性.
- 提出的优化和规范化方法有助于提高心电图分类的稳定性和准确性.
结论:
- 集成的MobileNetv2模型与提议的优化算法V5和提议的规范化方法V5为儿科心电图分析提供了强大的解决方案.
- 这些发现表明,开发的方法可以泛用于更复杂和多样化的心电图数据集,有助于准确检测心律失常.
相关概念视频
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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01:29Disturbances in Heart Rhythm
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01:28Dysrhythmias II: Classification of Tachyarrhythmias
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01:15Dysrhythmias I: Introduction
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01:30Dysrhythmias V: Evaluating Dysrhythmias
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01:25Correlation between ECG and Cardiac Cycle
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A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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