基于模拟的退化光学参数振荡器辅助神经网络强大的QRS检测
Zhiqiang Liao1,2, Zhuozheng Shi2,3, Md Shamim Sarker1,2
1Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
Heliyon
|April 5, 2024
概括
这项研究引入了一种新的模拟退化单元 (SDU) 辅助卷积神经网络 (CNN),以提高心电图 (ECG) 的准确性. SDU增强的CNN显著提高了QRS复杂检测性能和用于心血管疾病诊断的噪声强度.
科学领域:
- 生物医学工程 生物医学工程
- 心血管信号处理系统
- 医疗保健中的人工智能
背景情况:
- 在心电图 (ECG) 中精确检测QRS复合体对于诊断心血管疾病至关重要.
- 神经网络方法对QRS检测有希望,但对心电图记录中的噪声敏感.
- 现有的方法因ECG采集过程中不可避免的噪音而遭受性能恶化.
研究的目的:
- 提高基于神经网络的QRS检测器对噪声的稳定性和性能.
- 为改进QRS检测引入一种新的模拟退化单元 (SDU) 辅助卷积神经网络 (CNN).
主要方法:
- 开发了一个CNN模型,其中包含一个模拟退化单元 (SDU).
- SDU模拟光脉冲退化,以有效地抑制带内噪声.
- 在三个开源心电图数据库和现实世界噪音注入测试中评估了SDU增强的CNN.
主要成果:
- 与其他近期方法相比,SDU增强的CNN在QRS复杂检测方面表现优越.
- 对多个数据库的全面评估证实了拟议方法的有效性.
- 噪声注入测试显示,配备SDU的CNN的最佳噪声强度边界高出167-300%.
结论:
- SDU辅助的CNN在强大而准确的QRS检测方面取得了重大进展.
- 这种方法提供了增强的噪声弹性,对于临床环境中可靠的心电图分析至关重要.
- 增强SDU的CNN代表了通过ECG改进心血管疾病检测的有希望的工具.
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