通过粒子群优化和封闭反复单位改进的变化模式分解混合模型重建大动脉压力波形
Shuo Du1,2, Guozhe Sun3, Hongming Sun2
1College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China.
Physiological measurement
|September 4, 2025
概括
一个新的混合模型,PSO-VMD-GRU,从手臂压力波形 (BPW) 准确地重建了主动脉压力波形 (APW). 这种方法显示出改善心血管疾病监测和诊断的巨大潜力.
科学领域:
- 生物医学工程
- 心血管生理学
- 信号处理
- 医学的人工智能
背景情况:
- 大动脉压波形 (APW) 对于诊断和治疗心血管疾病至关重要.
- 目前用于APW估计的非侵入性方法需要提高准确性和实用性.
- 从手臂压力波形 (BPW) 准确的APW重建是一个重要的临床需求.
研究的目的:
- 开发和验证一种新的混合模型,即粒子集群优化-变量模式分解-门循环装置 (PSO-VMD-GRU),用于从BPW重建APW.
- 将拟议的PSO-VMD-GRU模型与通用转移函数 (GTF) 和其他神经网络 (TCN,CBi-SAN) 等已建立的方法进行比较.
- 评估模型在重建总波形和关键血液动力学指数 (静脉压,静脉压和脉冲压) 的准确性.
主要方法:
- 开发了一种混合模型,将PSO-VMD用于信号分解和GRU用于波形重建.
- 该模型使用侵入性APW和BPW数据进行训练和验证,并添加合成数据.
- 使用总波形和血液动力学指数的平均绝对误差 (MAE) 和斯皮尔曼相关系数 (SCC) 来评估性能.
主要成果:
- 在PSO-VMD-GRU中,总波形 (0.9912) 和透气压 (0.9676) 的SCC是最高的.
- 时间卷积网络 (TCN) 在静脉压 (0. 9850) 和脉冲压 (0. 9875) 中表现最好.
- 在所有指标上与CBi-SAN相匹配,并在特定参数中表现优于GTF和TCN.
结论:
- 通过将PSO-VMD与GRU整合,有效地提高了BPW的APW重建的准确性.
- 拟议的PSO-VMD-GRU模型为非侵入性血液动力学监测提供了一个有希望,准确和实用的方法.
- 这种混合模型的进一步验证和临床实施可以显著提高心血管诊断.
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