Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning - 2021

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Research Paper on Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Research Area:  Machine Learning

Abstract:

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG ca

Keywords:  
Cuffless Blood Pressure
Deep Learning
Convolutional Neural Network
Long Short-Term Memory
Blood Pressure Estimation
Photoplethysmogram
Remote Photoplethysmogram
Imaging Photoplethysmogram
Arterial Blood Pressure

Author(s) Name:  Fabian Schrumpf ,Patrick Frenzel ,Christoph Aust ,Georg Osterhoff and Mirco Fuchs

Journal name:   Sensors

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/s21186022

Volume Information:  Volume 21 Issue 18

Paper Link:   https://www.mdpi.com/1424-8220/21/18/6022

Related Papers