BrainZ-BP: A Noninvasive Cuff-Less Blood Pressure Estimation Approach Leveraging Brain Bio-Impedance and Electrocardiogram

被引:0
|
作者
Yang, Bufang [1 ]
Liu, Le [1 ]
Wu, Wenxuan [1 ]
Zhou, Mengliang [2 ]
Liu, Hongxing [1 ]
Ning, Xinbao [1 ]
机构
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210023, Peoples R China
[2] Nanjing Univ, Nanjing Jinling Hosp, Affiliated Hosp, Dept Neurosurg,Med Sch, Nanjing 210993, Peoples R China
基金
中国国家自然科学基金;
关键词
Estimation; Monitoring; Biomedical monitoring; Wrist; Feature extraction; Voltage measurement; Blood pressure; Brain bio-impedance (BIOZ); cuff-less blood pressure (BP) estimation; electrocardiogram (ECG); intracranial pressure (ICP); noninvasive monitoring; pulse transit time (PTT); SYSTEM; TIME;
D O I
10.1109/TIM.2023.3346527
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases (CVDs). Noninvasive and cuff-less BP estimation algorithm has gained much attention in recent years. Previous studies have demonstrated that brain bio-impedance (BIOZ) is a promising technique for noninvasive intracranial pressure (ICP) monitoring. Clinically, treatment for patients with traumatic brain injuries (TBI) requires monitoring the ICP and BP of patients simultaneously. Estimating BP by brain BIOZ directly can reduce the number of sensors attached to the patients, thus improving their comfort. To address the issues, in this study, we explore the feasibility of leveraging brain BIOZ for BP estimation and propose a novel cuff-less BP estimation approach called BrainZ-BP. Two electrodes are placed on the forehead and occipital bone of the head in the anterior-posterior direction for brain BIOZ measurement. Various features, including pulse transit time (PTT) and morphological features of brain BIOZ, are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient of random forest (RF) regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure estimation and are 1.71 mmHg, 3.02 mmHg, and 0.89 for diastolic pressure estimation. The presented BrainZ-BP can be applied in the brain BIOZ-based ICP monitoring scenario to monitor BP simultaneously.
引用
收藏
页码:1 / 13
页数:13
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