BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram

• URL: http://arxiv.org/abs/2111.14558v1
• Date: Mon, 29 Nov 2021 14:43:58 GMT
• Title: BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram
• Authors: Rishi Vardhan K, Vedanth S, Poojah G, Abhishek K, Nitish Kumar M, Vineeth Vijayaraghavan
• Abstract summary: Blood pressure is one of the most influential bio-markers for cardiovascular diseases and stroke. Current cuffless approaches to continuous BP monitoring involve explicit feature engineering surrounding Photoplethysmogram signals. We present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform.
• Score: 0.06524460254566904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform.

Related papers

• 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)
• Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation [55.8930142490617]
  We propose a decoder for QLDPC codes based on BP guided decimation (BPGD) BPGD significantly reduces the BP failure rate due to non-convergence.
  arXiv  Detail & Related papers  (2023-12-18T05:58:07Z)
• On the Convergence of Certified Robust Training with Interval Bound Propagation [147.77638840942447]
  We present a theoretical analysis on the convergence of IBP training. We show that when using IBP training to train a randomly two-layer ReLU neural network with logistic loss, gradient descent can linearly converge to zero robust training error.
  arXiv  Detail & Related papers  (2022-03-16T21:49:13Z)
• OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines [48.547200649819615]
  We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models.
  arXiv  Detail & Related papers  (2022-02-16T19:18:42Z)
• A Theoretical View of Linear Backpropagation and Its Convergence [55.69505060636719]
  Backpropagation (BP) is widely used for calculating gradients in deep neural networks (DNNs) Recently, a linear variant of BP named LinBP was introduced for generating more transferable adversarial examples for performing black-box attacks. We provide theoretical analyses on LinBP in neural-network-involved learning tasks, including adversarial attack and model training.
  arXiv  Detail & Related papers  (2021-12-21T07:18:00Z)
• 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 Deep Learning Approach to Predict Blood Pressure from PPG Signals [10.028103259763352]
  Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. We propose an advanced data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals.
  arXiv  Detail & Related papers  (2021-07-30T22:45:34Z)
• 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)
• Continuous Monitoring of Blood Pressure with Evidential Regression [19.92542487970484]
  Photoplethysmogram (MIC) signal-based blood pressure estimation is a promising candidate for modern BP measurements. The proposed method provides the reliability of the predicted BP by estimating its uncertainty to help diagnose medical condition.
  arXiv  Detail & Related papers  (2021-02-06T09:09:31Z)
• Belief Propagation Neural Networks [103.97004780313105]
  We introduce belief propagation neural networks (BPNNs) BPNNs operate on factor graphs and generalize Belief propagation (BP) We show that BPNNs converges 1.7x faster on Ising models while providing tighter bounds. On challenging model counting problems, BPNNs compute estimates 100's of times faster than state-of-the-art handcrafted methods.
  arXiv  Detail & Related papers  (2020-07-01T07:39:51Z)
• PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks [1.0045192779791103]
  We develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram signals. We present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg. The more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics.
  arXiv  Detail & Related papers  (2020-05-04T17:22:44Z)

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.