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A reinforcement learning algorithm used in analog spiking neural network for an adaptive cardiac Resynchronization Therapy device

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4 Author(s)
Qing Sun ; Institut d'Electronique du Solide et des Systemes - UMR 7163, Joint laboratory of the University of Strasbourg / CNRS - France ; Francois Schwartz ; Jacques Michel ; Yannick Herve

The target of this research is to develop an analog spiking neural network in order to improve the performance of biventricular pacemakers, which is also known as Cardiac Resynchronization Therapy (CRT) devices. By using the reinforcement learning algorithm, this paper proposes an approach improving cardiac delay predictions in every cardiac period so as to assist the CRT device to provide real-time optimal heartbeats. The simulation of the reinforcement learning algorithm has also been carried out and illustrated.

Published in:

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

Date of Conference:

May 30 2010-June 2 2010