Implantation study of an analog spiking neural network in an auto-adaptive pacemaker

Sun, Qing; Schwartz, François; Michel, J.; Rom, R.

doi:10.1109/newcas.2008.4606316

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Along the same lines of focusing on timing behavior of the contractions between atria and ventricles, Sun et al [50] propose a spiking neural network (SNN) based pacemaker device which predicts the pacing delays. The analog design of the SNN neurons uses a second order transfer function to determine the delays of the delivered impulses.…”

Section: B Control-based Approaches To Pacemaker Designmentioning

confidence: 99%

“…More precisely, it has been observed that the heart rate variability of healthy individuals is neither periodic, nor fully chaotic, but instead characterized by fractal laws [19][20] [21] [28]. On the other hand, while ignoring the fractal characteristics of the heart rate variability, the proposed approaches for artificial pacing have focused mainly on employing classical linear system theory [14][32] [37] [38] or neural networks [50] to design various control algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

Bogdan

Jain

Goyal

et al. 2012

2012 IEEE/ACM Third International Conference on Cyber-Physical Systems

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Managing cardiac disease and abnormal heart rate variability remain challenging problems with an enormous economic and psychological impact worldwide. Consequently, the purpose of this paper is to introduce a fractal approach to pacemaker design based on the constrained finite horizon optimal control problem. This is achieved by modeling the heart rate dynamics via fractional differential equations. Also, by using calculus of variations, we show that the constrained finite horizon optimal control problem can be reduced to solving a linear system. Finally, we discuss the hardware complexity involved in the practical implementation of fractal controllers.

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Section: B Control-based Approaches To Pacemaker Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

Bogdan

Jain

Goyal

et al. 2012

2012 IEEE/ACM Third International Conference on Cyber-Physical Systems

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

Sun

Schwartz

Michel

et al. 2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

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Implementation Study of an Analog Spiking Neural Network for Assisting Cardiac Delay Prediction in a Cardiac Resynchronization Therapy Device

Sun

Schwartz

Michel

et al. 2011

IEEE Trans. Neural Netw.

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In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

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Implantation study of an analog spiking neural network in an auto-adaptive pacemaker

Cited by 3 publications

References 6 publications

Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

Implantable Pacemakers Control and Optimization via Fractional Calculus Approaches: A Cyber-Physical Systems Perspective

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

Implementation Study of an Analog Spiking Neural Network for Assisting Cardiac Delay Prediction in a Cardiac Resynchronization Therapy Device

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