卷积神经网络通过微分演变优化,用于心电图分类
Shan Wei Chen1,2, Shir Li Wang1,3, XiuZhi Qi4
1Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia.
概括
这项研究优化了心电图 (ECG) 分类,使用一维卷积神经网络 (1D-CNN),增强了微分演化 (DE) 算法. 优化的1D-CNN显著提高了准确性,并减少了在远程医疗心血管护理中检测心律失常的培训时间.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- COVID-19大流行加速了远程医疗的采用,使远程患者监测变得至关重要.
- 自动心电图 (ECG) 分类是心血管疾病管理的关键远程医疗干预.
- 卷积神经网络 (CNN) 显示了对心电图分析的潜力,但需要优化精度和速度.
研究的目的:
- 提出和评估一维CNN (1D-CNN),通过微分演变 (DE) 算法优化,用于心律失常的分类.
- 在远程医疗框架内提高ECG分类的准确性和减少培训时间.
- 使用标准心律失常数据库,比较优化的1D-CNN与未优化的版本的性能.
主要方法:
- 开发了一个1D-CNN模型用于ECG心律失常的分类.
- 利用差异演化 (DE) 算法来优化1D-CNN的激活函数和参数.
- 在MIT-BIH和SCDH心律失常数据库上评估了优化的1D-CNN,将结果与未优化的模型进行比较.
主要成果:
- 优化DE的1D-CNN在心律分类方面取得了更高的准确性,在MIT-BIH数据库中从97.6%提高到99.5%,在SCDH数据库中从80.2%提高到88.5%.
- 与未优化的模型相比,优化的1D-CNN显示了训练时间的显著减少,MIT-BIH的训练时间减少了67.2%,SCDH的训练时间减少了64.2%.
- 使用ReLU激活功能和10个时代,使用9.22s (MIT-BIH) 和10.35s (SCDH) 的训练时间,注意到了具体的改进.
结论:
- 不同进化算法有效优化1D-CNNs,提高了ECG失常症分类的准确性和效率.
- 这种优化的方法代表了远程医疗心血管保健的重大进步,使得远程诊断更可靠,更快速.
- 优化1D-CNN的增强性能支持其在现实世界远程医疗系统中的应用,以获得更好的患者结果.
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