Office of Technology Transfer – University of Michigan

Corticocardiac Coupling as a Risk Factor for Sudden Death

Technology #6687

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Categories
Researchers
Jimo Borjigin
Managed By
Drew Bennett
Associate Director - Software Licensing 734-615-4004
Publications
Asphyxia-activated corticocardiac signaling accelerates onset of cardiac arrest.
Proc Natl Acad Sci U S A. 2015 Apr 21;112(16):E2073-82. doi: 10.1073/pnas.1423936112. Epub 2015 Apr 6., 2015

Sudden death occurs in more than 400,000 Americans annually and is a significant and under-recognized consequence of stroke. Identification of patients at risk for sudden death after stroke poses a major challenge. Recently, an animal model of stroke uncovered a surge in synchronous brain and heart electrical activity (corticocardiac coupling) hours before sudden death. This finding was absent in rats that survived ischemic brain injury. These results therefore suggest that the increased corticocardiac coupling represents an important biomarker for stroke-induced sudden death.

Monitoring of Corticocardiac Coupling

The brain-heart monitoring device predicts susceptibility for incurring sudden death by analyzing patterns of electrical coherence. Real-time monitoring is performed using simultaneous measurements of brain and heart electrical activity via electroencephalogram (EEG) and electrocardiogram (ECG/EKG), respectively. Software analysis of these signals computes both an electrocardiomatrix (ECM) and an electroencephalomatrix (EEM) to create clear visual representations of corticocardiac coherence. Use of algorithms to map brain-heart coherence may help predict risk of sudden death in patients treated at operating rooms, emergency rooms, and intensive care units. Thus, this non-invasive diagnostic and analytic device permits timely intervention to save lives of monitored patients.

Applications

  • Prevention of sudden death in patients at risk for:
    • Stroke
    • Seizure
    • Cardiac arrest

Advantages

  • Non-invasive
  • Real-time diagnostics
  • Simple visual data representations