Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation

J Med Syst. 2019 Dec 10;44(1):18. doi: 10.1007/s10916-019-1514-z.

Abstract

This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmonary resuscitation (CPR). We have designed a system that carries out Ear PPG for estimation of BP. In particular, the BP signals are estimated according to a long short-term memory (LSTM) model using an Ear PPG. To investigate the proposed method, two statistical analyses were conducted for comparison between BP measured by the micromanometer-based gold standard method (BPMEAS) and the Ear PPG-based proposed method (BPEST) for swine cardiac model. First, Pearson's correlation analysis showed high positive correlations (r = 0.92, p < 0.01) between BPMEAS and BPEST. Second, the paired-samples t-test on the BP parameters (systolic and diastolic blood pressure) of the two methods indicated no significant differences (p > 0.05). Therefore, the proposed method has the potential for estimation of BP for CPR biofeedback based on LSTM using a single Ear PPG.

Keywords: Biofeedback; Blood pressure (BP); Cardiopulmonary resuscitation (CPR); Earlobe photoplethysmography (ear PPG); Long short-term memory (LSTM).

MeSH terms

  • Artificial Intelligence*
  • Biofeedback, Psychology
  • Blood Pressure Determination / methods*
  • Cardiopulmonary Resuscitation*
  • Feasibility Studies
  • Humans
  • Photoplethysmography / instrumentation*