Investigation of Temporal Relationship between Cardiovascular Variables for Cuffless Blood Pressure Estimation
文献类型：会议论文
| 作者 | Yali Zheng; Carmen C. Y. Poon; Yuan-Ting Zhang |
| 出版日期 | 2012 |
| 会议名称 | Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2021) |
| 会议地点 | Hong Kong and Shenzhen, China |
| 英文摘要 | Nowadays, non-invasive blood pressure (BP) is commonly measured by cuff-based devices, which cause discomfort during measurement and can only provide snapshots of BP. In order to measure beat-to-beat BP continuously and cufflessly, models based on pulse arrival time (PAT) and RR interval (RRI), which can both be conveniently acquired by wearable devices, have been previously proposed. Nevertheless, the existing models often estimate BP based on PAT and RRI of the same beat and ignore the possible time shifts between these variables. In this paper, the cross correlation between RRI, systolic BP (SBP), diastolic BP (DBP) and PAT were studied on 25 young healthy subjects. In total, 1015 cross correlation coefficients, each from a 1-minute signal recording, were calculated for any two cardiovascular variables. The results of the study showed that on average, maximum correlation is found between RRI and DBP when RRI led by 1.3 seconds and between RRI and PAT or SBP when RRI led them by 2.5 seconds. Based on the results of this study, it is therefore concluded that future BP estimation models should take into account the time shifts between these cardiovascular variables when using RRI for the estimation of SBP and DBP as well as when using PAT for the estimation of DBP. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4048]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2012 |
| 推荐引用方式 GB/T 7714 | Yali Zheng,Carmen C. Y. Poon,Yuan-Ting Zhang. Investigation of Temporal Relationship between Cardiovascular Variables for Cuffless Blood Pressure Estimation[C]. 见:Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2021). Hong Kong and Shenzhen, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
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