ArticleScientific reports2019
Pulse-Wave-Pattern Classification with a Convolutional Neural Network.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Noninvasive Pulse Measurements for Cardiovascular Health Monitoring and Diagnoses.Advances in experimental medicine and biology · 2026Review
- A Hybrid Network Integrating 1DCNN, GRU, and ISENet for Pulse Signal Pattern Analysis.Journal of pregnancy · 2026Article
- [Research on type 2 diabetes prediction algorithm based on photoplethysmography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN.Scientific reports · 2024Article
- Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.Biomedical engineering online · 2024Article
- Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning.Biomedical engineering online · 2024Article
- Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications.Sensors (Basel, Switzerland) · 2023Article
- Soft Electronics for Health Monitoring Assisted by Machine Learning.Nano-micro letters · 2023Review
- Development of evaluation system for cerebral artery occlusion in emergency medical services: noninvasive measurement and utilization of pulse waves.Scientific reports · 2023Article
- Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs.Frontiers in bioengineering and biotechnology · 2023Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments.Frontiers in physiology · 2022Article
- Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.European heart journal. Digital health · 2021Article
- Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study.JMIR medical informatics · 2021Article
- Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning.Frontiers in physiology · 2021Article
- Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.
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