基于霍奇金 - 哈克斯利模型和多目标鱼优化算法进行心律失常信号分类的深度VMD注意网络.
1School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
PloS one
|May 14, 2025
概括
这项研究引入了一种新的AI方法,用于使用先进的信号处理和心脏建模来对心律失常进行分类. 开发的模型实现了高精度,超越现有方法,并证明了对真实世界的数据的稳定性.
科学领域:
- 计算电生理学 计算电生理学
- 医疗保健中的人工智能
- 生物医学信号处理
背景情况:
- 目前用于心律失常的AI分类依赖于心电图数据,但往往忽略了心脏电生理学原理.
- 基于霍奇金-哈克斯利 (HH) 模型的有限元模型 (FEM) 对于模拟心脏电活动至关重要.
研究的目的:
- 开发一种人工智能驱动的心律失常分类模型,将心脏电生理学与先进的信号处理相结合.
- 增强心电图信号分解和特征提取,以提高分类准确度.
主要方法:
- 模拟心律失常使用基于霍奇金-哈克斯利 (HH) 的有限元模型 (FEM) 来产生合成心电图信号.
- 开发了一种多目标鱼优化算法 (MOCOA) - 变态分解 (VMD) 技术,以优化心电图信号分解.
- 构建了一个深度的VMD注意力网络,通过贝叶斯优化 (TPE) 进行优化,用于心律失常的分类.
主要成果:
- MOCOA-VMD模型实现了94.46%的精度,超过了EEMD,VMD,CNN和LSTM模型.
- 深度注意模型,微调贝叶斯优化,达到96.11%的峰值准确性.
- 该模型在MIT-BIH心律失常数据库中显示出强度和通用性.
结论:
- 拟议的深度VMD注意力建模和分类策略提供了一个有希望的AI驱动的方法来检测心律失常.
- 将数学心脏模型与先进的信号处理集成,显著提高了分类性能.
- 这种方法有可能在生物医学信号处理中得到更广泛的应用.
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