Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Muammar Sadrawi; Yin-Tsong Lin; Chien-Hung Lin; Bhekumuzi Mathunjwa; Ho-Tsung Hsin; Shou-Zen Fan; Maysam F Abbod; Jiann-Shing Shieh

doi:10.3390/s21186264

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Sensors (Basel). 2021 Sep 18;21(18):6264. doi: 10.3390/s21186264.

Authors

Muammar Sadrawi¹, Yin-Tsong Lin², Chien-Hung Lin², Bhekumuzi Mathunjwa¹, Ho-Tsung Hsin^{1

3}, Shou-Zen Fan⁴, Maysam F Abbod⁵, Jiann-Shing Shieh¹

Affiliations

¹ Department of Mechanical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan.
² AI R&D Department, New Era AI Robotic Inc., Taipei 105, Taiwan.
³ Cardiovascular Intensive Care Unit, Far-Eastern Memorial Hospital, New Taipei City 220, Taiwan.
⁴ Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
⁵ Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK.

Abstract

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson's linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system-the systolic blood pressure (SBP) and diastolic blood pressures (DBP)-the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system-the systolic and diastolic pressures-the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

Keywords: arterial blood pressure; central venous pressure; deep convolutional autoencoder; electrocardiography; hemodynamics; intracranial pressure; non-invasive system; pulmonary arterial pressure.

MeSH terms

Blood Pressure
Blood Pressure Determination*
Electrocardiography*
Hemodynamics
Neural Networks, Computer