Synchronization of a Soft Robotic Ventricular Assist Device to the Native Cardiac Rhythm Using an Epicardial Electrogram

Abstract

Soft robotic devices have been proposed as an alternative solution for ventricular assistance. Unlike conventional ventricular assist devices (VADs) that pump blood through an artificial lumen, soft robotic VADs (SRVADs) use pneumatic artificial muscles (PAM) to assist native contraction and relaxation of the ventricle. Synchronization of SRVADs is critical to ensure maximized and physiologic cardiac output. We developed a proof-of-concept synchronization algorithm that uses an epicardial electrogram as an input signal and evaluated the approach on adult Yorkshire pigs (n = 2). An SRVAD previously developed by our group was implanted on the right ventricle (RV). We demonstrated an improvement in the synchronization of the SRVAD using an epicardial electrogram signal versus a RV pressure signal of 4 ± 0.5% in heart failure and 3.2 ± 0.5% during actuation for animal 1 and 7.4 ± 0.6% in heart failure and 8.2% ± 0.8% during actuation for animal 2. Results suggest that improved synchronization is translated in greater cardiac output. The pulmonary artery (PA) flow was restored to a 107% and 106% of the healthy baseline during RV electrogram actuation and RV pressure actuation, respectively, in animal 1, and to a 100% and 87% in animal 2. Therefore, the presented system using the RV electrogram signal as a control input has shown to be superior in comparison with the use of the RV pressure signal.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 20, 2020
Source ID
10.1115/1.4047114

Entities

People

  • Christopher J Payne
  • Conor J. Walsh
  • Daniel Bautista-Salinas
  • Isaac Wamala
  • Mossab Saeed
  • Nikolay V Vasilyev
  • Pedro J Del Nido
  • Peter E. Hammer
  • Thomas Thalhofer

Organizations

  • Harvard Medical School
  • Harvard University
  • United States Department of Defense

Tags

Fields of Study

  • Biology

Readers

  • Radio communications and signal processing.
  • Robotics and Automation.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • Autonomy