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Stabilization of the human heartbeat using adaptive controller-based optimized deep policy gradient.

Khalid A Alattas1

  • 1Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a new adaptive control system using high-order sliding mode control and deep reinforcement learning to stabilize cardiac rhythm. It improves robustness against disturbances for better arrhythmia management.

Keywords:
Closed-loop heartHeartbeat stabilizingHigh order sliding mode control (HO-SMC)Human heartOptimized deep policy gradient (ODPG)

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Area of Science:

  • Biomedical Engineering
  • Control Systems
  • Computational Neuroscience

Background:

  • Cardiac rhythm stabilization is crucial for cardiovascular health and preventing arrhythmias.
  • Traditional control methods face challenges due to the complex, dynamic nature of cardiac systems.
  • Physiological determinants and external factors introduce significant variability and disturbances.

Purpose of the Study:

  • To develop a novel closed-loop control framework for adaptive, real-time cardiac rhythm stabilization.
  • To integrate high-order sliding mode control (HO-SMC) with optimized deep policy gradient (ODPG) reinforcement learning.
  • To enhance controller robustness against cardiac rhythm uncertainties and disturbances.

Main Methods:

  • Combined HO-SMC with ODPG reinforcement learning for adaptive parameter tuning.
  • Utilized two neural networks (NNs) for dynamic adjustment of control gains.
  • Employed reinforcement learning to evaluate parameter configurations for system stabilization.
  • Validated the approach under diverse physiological and pathological conditions.

Main Results:

  • Demonstrated superior cardiac rhythm stabilization efficacy compared to conventional controllers.
  • Showcased enhanced robustness against uncertainties and disturbances through adaptive parameter tuning.
  • Validated the framework's effectiveness across various simulated physiological and pathological scenarios.

Conclusions:

  • Pioneered the adaptive synergy of sliding mode control and deep reinforcement learning for cardiac rhythm management.
  • The proposed intelligent control system offers a significant advancement in biomedical control.
  • This approach enables more effective management of cardiac rhythm irregularities.