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A short recorded pulse dataset for vascular age prediction in China
Qingfeng Tang1,2, Pengcheng Ding1, Guowei Dai3
1Digital and Intelligent Health Research Center, Anqing Normal University, Anqing, 246133, China.
Insights
Predicting vascular age (VA) using pulse signals offers a simple, non-invasive method for early cardiovascular disease risk assessment. This approach demonstrates high accuracy and stability in evaluating vascular health.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health
- Data Science
Background:
- Early assessment of cardiovascular disease (CVD) risk is crucial for prevention.
- Vascular age (VA) serves as a key indicator for early CVD risk screening.
Purpose of the Study:
- To present a novel pulse signal-based dataset for vascular age prediction.
- To evaluate the efficacy of statistical and artificial intelligence models in VA prediction using pulse signals.
Main Methods:
- A dataset of 1364 pulse cycles from 226 subjects (ages 20-69) was utilized.
- Pulse signals underwent denoising using Savitzky-Golay filters.
- Feature extraction involved calculating 4th-order derivatives of the pulse signal.
- Vascular age prediction was performed using the Klemera Doubal method (KDM) and five AI models.
Main Results:
- The applied models demonstrated high accuracy and stability in predicting vascular age.
- Pulse signal analysis proved to be an effective method for VA estimation.
Conclusions:
- Pulse signal analysis presents a simple, non-invasive, and effective strategy for vascular health assessment.
- This method facilitates early screening of cardiovascular disease risk through vascular age prediction.
Abstract:
Early assessment of cardiovascular disease risk plays an important role in preventing cardiovascular disease, vascular age (VA) is an important indicator for early screening of cardiovascular disease risk. This study presents a pulse signal-based dataset for VA prediction. The dataset comprises 226 subjects with 1364 pulse cycles, spanning both sexes (49.6% male, 50.4% female) and an age range of 20 to 69 years. Pulse signals were denoised by Savitzky-Golay filters, and 4th-order derivatives were calculated to extract the features of pulse signal. We applied the classic statistical model Klemera Doubal method (KDM) and five artificial intelligence models to predict VA. The experimental results showed that these models can predict VA with high accuracy and stability. It indicates that using pulse signals to predict VA is a simple, non-invasive, and effective method for assessing vascular health.
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