Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 262-271.doi: 10.11896/jsjkx.250400022

• Database & Big Data & Data Science • Previous Articles     Next Articles

Hypertension Recognition and Blood Pressure Prediction Based on Novel Hierarchical Ballistocar-diogram Signal Feature Set

ZHANG Yuchen1, YE Hanyu2, YAO Yuhan3, JIANG Rui1, YANG Gang4, ZHANG Xianchao5   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China
    3 College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
    4 Key Laboratory of High Speed Circuit Design and EMC Ministry of Education,Xidian University,Xi'an 710071,China
    5 Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province,Jiaxing University,Jiaxing,Zhejiang 314001,China
  • Received:2025-04-07 Revised:2025-07-26 Online:2026-07-15 Published:2026-07-10
  • About author:ZHANG Yuchen,born in 1989,Ph.D,lecturer,is a member of CCF(No.K8064M).His main research interests include biomedical informatics,biomedi-cal signal processing,and artificial in-telligence theory and applications.
    ZHANG Xianchao,born in 1984,Ph.D,professor,Ph.D supervisor.His main research interests include digital health,integrated information networks,and intelligent perception and control.
  • Supported by:
    General Projects of the National Natural Science Foundation of China(62472386) and Key Program of the Natural Science Foundation of Zhejiang Province,China(LD24F020009).

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Abstract: Accurate early identification of hypertension is crucial for preventing further progression of the disease.Ballistocardiogram(BCG) signals,which reflect the dynamic movement of the heart's center of mass due to blood flow during normal respiration and cardiac cycles,can precisely capture blood pressure(BP) variations.However,most existing BCG-based hypertension identification methods primarily rely on traditional analyses(e.g.,time-frequency or nonlinear domain techniques) to extract limi-ted features,which fail to comprehensively characterize BP-related signal patterns.In addition,most BCG-based studies focus on diagnosing hypertension rather than predicting precise BP values.To address these limitations,this paper proposes a novel multi-level BCG feature set incorporating time-frequency domain features,nonlinear domain features,fluctuation characteristics,and waveform features.Comparative analyses are conducted using classical machine learning models,such as random forest(RF),and mainstream deep learning architectures such as convolutional neural network(CNN) and deep neural network(DNN).The results demonstrate that the proposed feature set significantly improves hypertension identification:the RF model achieves an accuracy of 82.26%,and the CNN model reaches 83.57%,outperforming Liu et al.'s feature set(accuracy of 77.92% and 78.2%,respectively).For BP prediction,the DNN regression model achieves a root mean square error(RMSE) of 6.59 mmHg for diastolic blood pressure(DBP) and 3.99 mmHg for systolic blood pressure(SBP),both superior to results using Liu et al.'s feature set.Additionally,this study validates the impact of the BCG signal's segment length on the performance of BP analysis.These fin-dings highlight that the proposed feature engineering approach effectively captures BCG signal patterns critical for BP analysis,demonstrating the potential to computationally enable more convenient and accurate non-invasive,cuffless BP monitoring.

Key words: Ballistocardiogram signal, Feature engineering, Hypertension identification, Blood pressure prediction, Machine lear-ning

CLC Number: 

  • TP18
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