数字中医药（英文）2026，Vol.9Issue(3)：344-361,18.DOI:10.1016/j.dcmed.2026.08.002
面向轻量化血压预测的任务导向重采样脉搏波压缩
Task-oriented compression of resampled pulse waves for lightweight blood pressure prediction
摘要
Abstract
Objective This study proposes a task-oriented signal compression paradigm for non-inva-sive blood pressure(BP)prediction,eliminating manual feature engineering through resam-pling to achieve a high-performance lightweight model suitable for embedded systems. Methods The PDA-1 pulse diagnostic device equipped with a piezoelectric sensor was used to collect radial artery pulse waves from healthy volunteers at Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between November 20,2021 and November 19,2022.The most stable 8 s segment containing multiple complete pulse cycles was extracted from each 30 s recording.Original signals(200 Hz)were resampled to 40 Hz us-ing Fourier transform,wavelet transform(db9),and cubic spline interpolation.Three feature sets were constructed:baseline(resampled data and demographics),fusion(baseline plus handcrafted time-domain features),and handcrafted(handcrafted features and demograph-ics)feature sets.XGBoost and random forest(RF)models were developed with Bayesian opti-mization for hyperparameter tuning,and evaluated using 10-fold cross-validation.Prediction accuracy was assessed against British Hypertension Society(BHS)and Association for the Ad-vancement of Medical Instrumentation(AAMI)standards,while reconstruction quality was quantified by percentage root-mean-square difference(PRD).Model evaluation and multiple comparisons among different configurations were conducted using the corresponding statis-tical methods.Visual and interpretability analyses were performed using SHapley Additive ex-Planations(SHAP),frequency-energy distribution analysis,and wavelet multi-resolution de-composition. Results A total of 348 healthy volunteers were enrolled.XGBoost and RF models built on re-sampled data achieved clinically competitive performance,with XGBoost demonstrating su-perior accuracy and computational efficiency.The XGBoost model employing Fourier trans-form resampling and the baseline feature set achieved a mean absolute error(MAE)of(6.01±0.94)mmHg for systolic blood pressure(SBP)and(4.60±0.34)mmHg for diastolic blood pressure(DBP)on the test set,meeting BHS Grade B for SBP and BHS Grade A for DBP,as well as AAMI standards.No statistically significant differences in predictive accuracy were found among the three resampling methods(P>0.05);however,their reconstruction errors differed markedly:wavelet transform exhibited the highest error(PRD=35.52%),followed by Fourier transform(PRD=8.87%),and interpolation the lowest(PRD=1.33%).Compared with the baseline feature set,the fused feature set reduced SBP MAE by only 0.09 mmHg(correct-ed P>0.05)while increasing XGBoost training time by 92.5%-247.5%(P<0.001).The handcrafted feature set achieved comparable predictive performance to the baseline,indicat-ing that manual feature engineering is redundant when resampled signals are employed. Conclusion This study validated the sufficiency of compressed signals for clinical-grade pre-diction and the redundancy of handcrafted feature engineering.Integrating task-oriented compression with machine learning may support the development of lightweight wearable continuous BP monitoring devices.关键词
脉搏波/任务导向/重采样方法/特征工程/血压预测/轻量化Key words
Pulse waves/Task-oriented/Resampling method/Feature engineering/Blood pressure prediction/Lightweight引用本文复制引用
万小龙,李克松,孙晓军,崔骥,许家佗,胡晓娟..面向轻量化血压预测的任务导向重采样脉搏波压缩[J].数字中医药（英文）,2026,9(3):344-361,18.基金项目
National Science and Technology Major Project of China(2025ZD0545000),High-level Traditional Chinese Medi-cine Key Discipline Construction Project of the National Administration of Traditional Chinese Medicine:Tradi-tional Chinese Medicine Diagnostics(ZYYZDXK2023069),and Shanghai Key Discipline Construction Project of Tra-ditional Chinese Medicine(Clinical)(shzyyzdxk-2024207). （2025ZD0545000）
