Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms
8 affiliations
- 1Institute of Traditional Medicine, College of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- 2Department of Traditional Chinese Medicine, Taipei City Hospital RenAi Branch, Taipei, Taiwan
- 3Department of Recreation and Sport Management, University of Taipei, Taipei, Taiwan
- 4School of Chinese Medicine, College of Medicine, National Yang-Ming Chiao-Tung University, 155, Li-Nong St, Sec 2, Peitou, Taipei, Taiwan
- 5Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, No.43, Section 4, Keelung Road, Taipei, Taiwan
- 6Division of Hematology and Oncology, Department of Medicine, Taipei City Hospital, Renai Branch, Taipei, Taiwan
- 7Department of traditional Chinese medicine, National Yang-Ming Chiao-Tung University Hospital, Yilan, Taiwan
- 8Biomedical Engineering Research Center, National Defense Medical University, Taipei, Taiwan
Abstract
Background/Aim: Breast cancer (BC) remains a major health threat, highlighting the need for accessible screening. Characteristics of the cardiovascular system can be altered in BC patients. This study explored a noninvasive method combining arterial pulse-waveform analysis and classification to compare arterial pulse-waveform characteristics between BC patients (n = 51)) and age-matched controls (n = 76)).
Methods: From one-minute radial waveform recordings, 40 harmonic indices (amplitude proportions, phase angles, and their variability) were derived. Significant differences in pulse-waveform indices were observed between groups, suggesting altered pulse-wave transmission conditions in BC patients. Two classifiers were compared: machine learning and a novel pulse-distribution analysis (PDA).
Results: PDA demonstrated an acceptable discrimination with an AUC of 0.74, outperforming machine learning (AUC up to 0.58). Subgroup analysis by clinical factors such as TNM stage showed variable performance, with AUCs ranging from 0.66-0.93. Discrimination appeared to improve with disease progression (AUC 0.86 for stage IV), suggesting that tumor-induced vascular alterations may affect pulse-wave transmission.
Conclusion: The primary contribution of this work is the identification of distinct pulse-waveform signatures in BC patients, providing a physiological basis for future development of noninvasive screening tools. The proposed method, characterized by its low cost and ease of use, warrants further validation in larger cohorts.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s13239-026-00842-0.
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BibTeX
@article{chen2026discriminating,
title = {{Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms}},
author = {Chao-Tsung Chen and Ting-Wei Yeh and Yi-Sheng Chou and Chi-Feng Cheng and Yi-Hsuan Lai and Chung-Hua Hsu and Hsin Hsiu},
journal = {Cardiovascular Engineering and Technology},
year = {2026},
month = may,
doi = {10.1007/s13239-026-00842-0},
url = {https://doi.org/10.1007/s13239-026-00842-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms
AU - Chao-Tsung Chen
AU - Ting-Wei Yeh
AU - Yi-Sheng Chou
AU - Chi-Feng Cheng
AU - Yi-Hsuan Lai
AU - Chung-Hua Hsu
AU - Hsin Hsiu
PY - 2026
DA - 2026/05/18
JO - Cardiovascular Engineering and Technology
DO - 10.1007/s13239-026-00842-0
UR - https://doi.org/10.1007/s13239-026-00842-0
AB - Background/Aim: Breast cancer (BC) remains a major health threat, highlighting the need for accessible screening. Characteristics of the cardiovascular system can be altered in BC patients. This study explored a noninvasive method combining arterial pulse-waveform analysis and classification to compare arterial pulse-waveform characteristics between BC patients (n = 51)) and age-matched controls (n = 76)). Methods: From one-minute radial waveform recordings, 40 harmonic indices (amplitude proportions, phase angles, and their variability) were derived. Significant differences in pulse-waveform indices were observed between groups, suggesting altered pulse-wave transmission conditions in BC patients. Two classifiers were compared: machine learning and a novel pulse-distribution analysis (PDA). Results: PDA demonstrated an acceptable discrimination with an AUC of 0.74, outperforming machine learning (AUC up to 0.58). Subgroup analysis by clinical factors such as TNM stage showed variable performance, with AUCs ranging from 0.66-0.93. Discrimination appeared to improve with disease progression (AUC 0.86 for stage IV), suggesting that tumor-induced vascular alterations may affect pulse-wave transmission. Conclusion: The primary contribution of this work is the identification of distinct pulse-waveform signatures in BC patients, providing a physiological basis for future development of noninvasive screening tools. The proposed method, characterized by its low cost and ease of use, warrants further validation in larger cohorts. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s13239-026-00842-0.
SN - 1869-408X
SN - 1869-4098
ER -