Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation

In medical field, many cardiovascular and correlated diseases can be early treated by monitoring and analyzing the subject’s blood pressure (BP). However, the measurement of blood pressure requires the use of invasive medical and health equipment, including the classical sphygmomanometer or the digital pressure meter. In this paper, we proposed an innovative algorithmic pipeline to properly estimate the systolic and diastolic blood pressure of a subject through the visio-reconstruction of the PhotoPlethysmoGraphic (PPG) signal. By means of an innovative method of face-motion magnification through Deep Learning, it is possible to visioreconstruct specific points of the PPG signal in order to extract features related to the pressure level of the analyzed subject. The proposed approach can be used effectively in healthcare facilities for the fast and noninvasive monitoring of the pressure level of subjects or in other similar applications. We compared our results using a classic cuff-less blood pressure device with encouraging results that reach 92% in accuracy.

[1]  Photoplethysmographic augmentation index using the signal fourth derivative , 2012, 2012 Computing in Cardiology.

[2]  Stephen R. Alty,et al.  Predicting Arterial Stiffness From the Digital Volume Pulse Waveform , 2007, IEEE Transactions on Biomedical Engineering.

[3]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[4]  Enric Monte-Moreno,et al.  Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques , 2011, Artif. Intell. Medicine.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Francesco Rundo,et al.  Advanced Motion-Tracking System with Multi-Layers Deep Learning Framework for Innovative Car-Driver Drowsiness Monitoring , 2019, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019).

[7]  G. Banna,et al.  Advanced Deep Learning Embedded Motion Radiomics Pipeline for Predicting Anti-PD-1/PD-L1 Immunotherapy Response in the Treatment of Bladder Cancer: Preliminary Results , 2019, Electronics.

[8]  Chung-Pin Li,et al.  High Blood Pressure and All-Cause and Cardiovascular Disease Mortalities in Community-Dwelling Older Adults , 2015, Medicine.

[9]  A. Addeo,et al.  Oral Metronomic Vinorelbine in Advanced Non-small Cell Lung Cancer Patients Unfit for Chemotherapy , 2018, AntiCancer Research.

[10]  Sebastiano Battiato,et al.  Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis , 2018, Comput..

[11]  Mitja Lustrek,et al.  Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network , 2019, Sensors.

[12]  Francesco Rundo,et al.  A Nonlinear Pattern Recognition Pipeline for PPG/ECG Medical Assessments , 2018, Sensors.

[13]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[14]  Frédo Durand,et al.  Learning-based Video Motion Magnification , 2018, ECCV.

[15]  Mohammad Kachuee,et al.  Non-invasive blood pressure estimation using phonocardiogram , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[16]  Roozbeh Jafari,et al.  Noninvasive Cuffless Blood Pressure Estimation Using Pulse Transit Time and Impedance Plethysmography , 2019, IEEE Transactions on Biomedical Engineering.

[17]  Francesco Rundo,et al.  PPG/ECG Multisite Combo System Based on SiPM Technology , 2018, Sensors.

[18]  Monte-MorenoEnric.  Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques , 2011 .

[19]  Sebastiano Battiato,et al.  An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment , 2018, Sensors.