BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram

• URL: http://arxiv.org/abs/2311.10996v2
• Date: Thu, 23 Nov 2023 05:08:16 GMT
• Title: BrainZ-BP: A Non-invasive Cuff-less Blood Pressure Estimation Approach Leveraging Brain Bio-impedance and Electrocardiogram
• Authors: Bufang Yang, Le Liu, Wenxuan Wu, Mengliang Zhou, Hongxing Liu, Xinbao Ning
• Abstract summary: Brain bio-impedance (BIOZ) is a promising technique for non-invasive intracranial pressure (ICP) monitoring. 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 and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest regression model are 2.17 mmHg, 3.91 mmHg, and 0.90 for systolic pressure
• Score: 2.295711231919421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and continuous blood pressure (BP) monitoring is essential to the early prevention of cardiovascular diseases. Non-invasive 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 non-invasive 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 and morphological features of brain BIOZ are extracted and fed into four regression models for BP estimation. Results show that the mean absolute error, root mean square error, and correlation coefficient of random forest 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.

Related papers

• A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage [42.24368372333753]
  We propose a novel prediction framework for measuring postoperative cerebral hemorrhage using only the patient's initial CT scan. Our method can generate follow-up CT scans better than state-of-the-art methods, and achieves an accuracy of 86.37% in predicting follow-up prognostic labels.
  arXiv  Detail & Related papers  (2024-08-01T22:08:52Z)
• Exploring the limitations of blood pressure estimation using the photoplethysmography signal [0.0]
  Photoplemography (N- Siamese) and Invasive Arterial Blood Pressure (N-IABP) signals are compared. N-IABP signals meet with AAMI standards for both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) Our findings highlight the potential and limitations of employing PPG for BP estimation.
  arXiv  Detail & Related papers  (2024-04-09T14:08:23Z)
• A Finger on the Pulse of Cardiovascular Health: Estimating Blood Pressure with Smartphone Photoplethysmography-Based Pulse Waveform Analysis [2.4347312660509672]
  This study introduces four innovative strategies to enhance smartphone-based photoplethysmography for blood pressure estimation (SPW-BP) We employ often-neglected data-quality improvement techniques, such as height normalization, corrupt data removal, and boundary signal reconstruction. Correlation and SHAP analysis identified key features for improving BP estimation. However, Bland-Altman analysis revealed systematic biases, and MAE analysis showed that the results did not meet AAMI and BHS accuracy standards.
  arXiv  Detail & Related papers  (2024-01-20T05:05:17Z)
• Phase-shifted remote photoplethysmography for estimating heart rate and blood pressure from facial video [4.392877207448768]
  Heart rate and blood pressure are important biometric information for the monitoring of cardiovascular system. In this thesis, we propose a vision-based method for estimating the heart rate and blood pressure. This thesis proposes a 2-stage deep learning framework consisting of a dual remote photoplethysmography network (BP-Net) and bounded blood pressure network (BBP-Net)
  arXiv  Detail & Related papers  (2024-01-09T13:56:37Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression [47.1405366895538]
  Sparse linear regression methods for high-dimensional data commonly assume that residuals have constant variance, which can be violated in practice. This paper proposes estimating high-dimensional heteroscedastic linear regression models using a heteroscedastic partitioned empirical Bayes Expectation Conditional Maximization algorithm.
  arXiv  Detail & Related papers  (2023-09-15T22:06:29Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals [1.1695966610359496]
  Most existing methods used in the hospitals for continuous monitoring of Blood Pressure (BP) are invasive. In this study, we explored the applicability of autoencoders in predicting BP from non-invasively collectible signals such as Photoplethysmogram ( PPG) and ECG signals. It was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with the state-of-the-art performance on a very large dataset.
  arXiv  Detail & Related papers  (2021-11-12T19:34:20Z)
• A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation [0.0]
  We developed a method for estimating blood pressure based on the features extracted from Electrocardiogram (ECG) signals and the Arterial Blood Pressure (ABP) data. We evaluated and compared the findings to create the model with the highest accuracy by applying the clustering approach. The results show that the proposed clustering approach helps obtain more accurate estimates of Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP)
  arXiv  Detail & Related papers  (2021-10-13T19:16:10Z)
• Ambulatory blood pressure monitoring versus office blood pressure measurement: Are there sex differences? [55.41644538483948]
  Office Blood Pressure Measurement (OBP) is a technique performed in-office with the sphygmomanometer, while Ambulatory Blood Pressure Monitoring (ABPM) is a technique that measures blood pressure during 24h. The aim of this study is to examine the possible influence of sex on the discrepancies between OBP and ABPM in 872 subjects with known or suspected hypertension.
  arXiv  Detail & Related papers  (2021-06-04T10:09:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.