集成技术2025，Vol.14Issue(3)：102-118,17.DOI:10.12146/j.issn.2095-3135.20250118001
基于Transformer编码器的脑血流速度重建模型研究
Research on A Transformer Encoder-Based Model for Cerebral Blood Flow Velocity Reconstruction
刘高城 1童嘉博 2杨仕林 1王秋颖 1唐新宇 3刘畅 4刘嘉4
作者信息
- 1. 中国科学院深圳先进技术研究院 深圳 518055||中国科学院大学 北京 100049
- 2. 澳门科技大学 澳门 999078
- 3. 中国科学院深圳先进技术研究院 深圳 518055||南方科技大学 深圳 518055
- 4. 中国科学院深圳先进技术研究院 深圳 518055
- 折叠
摘要
Abstract
Cerebral blood flow velocity(CBFV)reconstruction plays a crucial role in evaluating cerebrovascular function,particularly in the early diagnosis of cerebrovascular diseases,optimizing treatment plans,and preventing strokes.Existing CBFV reconstruction methods face challenges in accuracy and efficiency when processing multivariate time-series signals,particularly in the context of data scarcity and complex signal processing.This study proposes a multivariate time-series model based on a Transformer encoder,which achieves high-precision CBFV reconstruction using arterial blood pressure and CO2 time-series signals.The model design is based on a long short-term memory module,which effectively compensates for the limitations of the global attention mechanisms in processing local information and enhances local feature learning.Additionally,a hybrid loss function is employed to optimize local waveform errors,improving reconstruction accuracy.Furthermore,to address the issue of data scarcity in the target domain,this study introduces a transfer learning strategy based on the correlation between arterial blood pressure and electrocardiogram signals,alleviating the impact of limited data on model performance.Experimental results demonstrate that the proposed model outperforms traditional regression and deep learning models in the CBFV reconstruction task,with a Pearson correlation coefficient of 0.51870,a dynamic time warping distance of 17.879,and mutual information of 0.34375,while completing the reconstruction of 200 data points in 0.04 s.The study validates the effectiveness of this method in precision medicine and provides innovative solutions for clinical diagnosis,disease prevention,and personalized treatment,with broad application prospects,particularly in medical signal processing,intelligent healthcare,and health monitoring.关键词
脑血流速度重建/迁移学习/Transformer/长短期记忆网络Key words
reconstruction of cerebral blood flow velocity/transfer learning/Transformer/long short-term memory分类
医药卫生引用本文复制引用
刘高城,童嘉博,杨仕林,王秋颖,唐新宇,刘畅,刘嘉..基于Transformer编码器的脑血流速度重建模型研究[J].集成技术,2025,14(3):102-118,17.
