基于残余密度的卷积神经网络架构,以基于心电图信号识别心脏健康
Alaa E S Ahmed1,2, Qaisar Abbas1, Yassine Daadaa1
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种新的残余密集卷积神经网络 (RD-CNN),用于精确的心电图 (ECG) 分析. RD-CNN有效地分类心跳,提高了高性能的心血管疾病检测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 通过监测心脏电活动,对诊断心血管疾病至关重要.
- 现有的心电图心跳分类方法在性能上扎,特别是在不平衡的数据集上.
- 需要先进的深度学习模型来提高ECG解释的准确性和效率.
研究的目的:
- 开发一种高性能心电图心跳分类模型,使用一种新的卷积神经网络 (CNN).
- 为了利用残留和密集连接的综合优势,增强特征提取和梯度传播.
- 通过强大的心电图信号分析来改善各种心脏病的检测.
主要方法:
- 设计了一个残余密集卷积神经网络 (RD-CNN) 模型,将残余和密集的块与聚合层集成在一起.
- 电脑心电图数据进行了预处理,包括消除和重新采样技术,以解决文物和阶级不平衡.
- 使用线性支向量机器 (LSVM) 将心跳分为五个不同的类别.
- 用RD-CNN算法根据提取的特征对心电图数据进行分类.
主要成果:
- 拟议的RD-CNN模型实现了高性能指标:98.5%的准确性,97.6%的灵敏性,96.8%的特异性和0.99.9%的AUC.
- 在两个基准数据集上进行了广泛的模拟,验证了模型的有效性.
- 该方法与最近提出的几种用于心脏病检测的算法相比,表现出更高的性能.
结论:
- 开发的RD-CNN模型为自动ECG解释提供了一个轻量级和实用的解决方案.
- 该模型的高精度和效率使其适合在临床环境中进行持续监测.
- 这种方法可以显著支持心脏病学家更有效地诊断心血管疾病.
相关概念视频
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An ECG utilizes electrodes on the skin...
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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