ArticleBioMed research international2021
Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 44 citations in OpenAlex.
- Multimodal deep learning for cardiovascular disease detection using pulse wave and vocal signals: a prediction model development and validation study.BMC medical informatics and decision making · 2026Article
- Development and validation of a nomogram model integrating noninvasive detection of radial pulse wave for predicting diabetic foot risk in type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.Biomedical engineering online · 2025Article
- Impact of personalized accurate intensity exercise on radial artery pulse wave in patients with multi-chronic diseases: a holistic functional assessment approach.Journal of thoracic disease · 2025Article
- Fusing wrist pulse and ECG data for enhanced identification of coronary heart disease and its complications.Frontiers in physiology · 2025Article
- Classification of coronary artery disease using radial artery pulse wave analysis via machine learning.BMC medical informatics and decision making · 2024Article
- Article
- Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability.Frontiers in neuroscience · 2024Article
- Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.BMC complementary medicine and therapies · 2023Article
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7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular diseases have been always the most common cause of morbidity and mortality worldwide. Health monitoring of high-risk and suspected patients is essential. Currently, invasive coronary angiography is still the most direct and accurate method of determining the severity of coronary artery lesions, but it may not be the optimal clinical choice for suspected patients who had clinical symptoms of coronary heart disease (CHD) such as chest pain but no coronary artery lesion. Modern medical research indicates that radial pulse waves contain substantial pathophysiologic information about the cardiovascular and circulation systems; therefore, analysis of these waves could be a noninvasive technique for assessing cardiovascular disease.
objectiveThe objective of this study was to analyze the radial pulse wave to construct models for assessing the extent of coronary artery lesions based on pulse features and investigate the latent value of noninvasive detection technology based on pulse wave in the evaluation of cardiovascular disease, so as to promote the development of wearable devices and mobile medicine.
methodThis study included 529 patients suspected of CHD who had undergone coronary angiography. Patients were sorted into a control group with no lesions, a 1 or 2 lesion group, and a multiple (3 or more) lesion group as determined by coronary angiography. The linear time-domain features and the nonlinear multiscale entropy features of their radial pulse wave signals were compared, and these features were used to construct models for identifying the range of coronary artery lesions using the
results(1) Compared with the control group, the group with 1 or 2 lesions had increases in their radial pulse wave time-domain features H2/H1, H3/H1, and W2 (
conclusionPulse wave signal can identify the range of coronary artery lesions with acceptable accuracy; this result is promising valuable for assessing the severity of coronary artery lesions. The technique could be used to development of mobile medical treatments or remote home monitoring systems for patients suspected or those at high risk of coronary atherosclerotic heart disease.
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