ArticleJMIR medical informatics2020
Machine Learning-Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.BMC complementary medicine and therapies · 2023Article
- Digital Transformation in the Diagnostics and Therapy of Cardiovascular Diseases: Comprehensive Literature Review.JMIR cardio · 2023Review
- Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring.Scientific reports · 2022Article
- Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.BioMed research international · 2022Article
- Few-shot pulse wave contour classification based on multi-scale feature extraction.Scientific reports · 2021Article
- A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals.
objectiveThe objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data.
methodsVarious machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier.
resultsIt was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted.
conclusionsWe expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.
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Registered trials
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