A short recorded pulse dataset for vascular age prediction in China
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 compr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05598-1 |
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| author | Qingfeng Tang Pengcheng Ding Guowei Dai Liangliang Zhang Guangjun Wang Benyue Su Xiaojuan Hu Ji Cui Haoyu Qu Hui An |
| author_facet | Qingfeng Tang Pengcheng Ding Guowei Dai Liangliang Zhang Guangjun Wang Benyue Su Xiaojuan Hu Ji Cui Haoyu Qu Hui An |
| author_sort | Qingfeng Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fb721bc69a5b45e1ad7dff2328498bf1 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-fb721bc69a5b45e1ad7dff2328498bf12025-08-20T04:01:43ZengNature PortfolioScientific Data2052-44632025-07-011211910.1038/s41597-025-05598-1A short recorded pulse dataset for vascular age prediction in ChinaQingfeng Tang0Pengcheng Ding1Guowei Dai2Liangliang Zhang3Guangjun Wang4Benyue Su5Xiaojuan Hu6Ji Cui7Haoyu Qu8Hui An9Digital and Intelligent Health Research Center, Anqing Normal UniversityDigital and Intelligent Health Research Center, Anqing Normal UniversityCollege of Computer Science, Sichuan UniversityDigital and Intelligent Health Research Center, Anqing Normal UniversityDigital and Intelligent Health Research Center, Anqing Normal UniversityDigital and Intelligent Health Research Center, Anqing Normal UniversityShanghai Innovation Center of TCM health Service, Shanghai University of Traditional Chinese MedicineSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese MedicineSchool of Informatics, Hunan University of Chinese MedicineHealth Management & Physical Examination Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and ScienceAbstract 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.https://doi.org/10.1038/s41597-025-05598-1 |
| spellingShingle | Qingfeng Tang Pengcheng Ding Guowei Dai Liangliang Zhang Guangjun Wang Benyue Su Xiaojuan Hu Ji Cui Haoyu Qu Hui An A short recorded pulse dataset for vascular age prediction in China Scientific Data |
| title | A short recorded pulse dataset for vascular age prediction in China |
| title_full | A short recorded pulse dataset for vascular age prediction in China |
| title_fullStr | A short recorded pulse dataset for vascular age prediction in China |
| title_full_unstemmed | A short recorded pulse dataset for vascular age prediction in China |
| title_short | A short recorded pulse dataset for vascular age prediction in China |
| title_sort | short recorded pulse dataset for vascular age prediction in china |
| url | https://doi.org/10.1038/s41597-025-05598-1 |
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