ORIGINAL RESEARCH article

Front. Physiol.

Sec. Computational Physiology and Medicine

Volume 16 - 2025 | doi: 10.3389/fphys.2025.1628309

Fusing Pulse and ECG Data for Enhanced Identification of Coronary Heart Disease and Its Complications

Provisionally accepted
leixin  hongleixin hong1,2Wenjie  WuWenjie Wu2Xia  ChenXia Chen3Danqun  XiongDanqun Xiong3Xiangdong  XuXiangdong Xu3*Jianjun  YanJianjun Yan4*Rui  GuoRui Guo2*
  • 1Shanghai University of Traditional Chinese Medicine, Shanghai, China
  • 2Shanghai University of Traditional Chinese Medicine, shanghai, China
  • 3Shanghai Jiading District Central Hospital, shanghai, China
  • 4School of Mechanical and Power Engineering, East China University of Science and Technology, shanghai, China

The final, formatted version of the article will be published soon.

This study aimed to explore the potential of synchronously acquiring wrist pressure pulse wave (PPW) and limb lead electrocardiogram (ECG) signals for the development of an identification model for coronary heart disease (CHD) and its associated comorbidities. Methods: A custom-designed device equipped with pressure and ECG sensors, was utilized to synchronously collect wrist PPW and limblead ECG signals from 463 participants. Features were extracted from these two types of physiological signals to form distinct datasets, and different RF models were built based on these datasets. The top-performing RF model was then selected and compared against the Feature-Selected (FS-RF), Support Vector Machine (SVM) and Bagged Decision Tree (BDT) models. Ultimately, the optimal model for predicting coronary heart disease (CHD) and its comorbidity was determined based on evaluation metrics.The RF model that integrated both PPW and ECG features demonstrated significantly higher effectiveness compared to the RF model that relied on a single physiological signal. Furthermore, when benchmarked against the feature-selected RF(FS-RF), SVM and DBT models, the FS-RF model demonstrated the best performance, achieving an accuracy of 76.32%, an average precision of 75.82%, an average recall of 76.11%, and an average F1-score of 75.88%, all of which were higher than those of other models. Notably, the selected feature by FS-RF encompassed both PPW and ECG features.This study highlights the importance of synchronously acquiring of PPW and ECG signal, along with feature selection, in enhancing the performance of the FS-RF model for identifying CHD and its associated conditions. These findings provide a scientific basis for the application of wearable devices in clinical settings, highlighting their potential to aid in the early detection and management of cardiovascular disease.

Keywords: coronary heart disease, complications, Synchronous acquisition of ECG and PPW, machine learning algorithms, modeling

Received: 15 May 2025; Accepted: 08 Jul 2025.

Copyright: © 2025 hong, Wu, Chen, Xiong, Xu, Yan and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xiangdong Xu, Shanghai Jiading District Central Hospital, shanghai, China
Jianjun Yan, School of Mechanical and Power Engineering, East China University of Science and Technology, shanghai, China
Rui Guo, Shanghai University of Traditional Chinese Medicine, shanghai, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.