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Adaptive heartbeat regulation using double deep reinforcement learning in a Markov decision process framework.

Walid Ayadi1, Emad Alkhazraji2, Haitham Khaled1

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This study introduces an adaptive nonlinear disturbance compensator (ANDC) with double deep reinforcement learning (DDRL) to stabilize heart rhythms. The novel approach effectively manages cardiac activity under various conditions, outperforming traditional methods.

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Adaptive nonlinear disturbance compensator (ANDC)Cardiovascular systemDouble deep reinforcement learning (DDRL)HeartbeatMarkov decision process (MDP)

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Control Systems

Background:

  • Cardiac rhythm irregularities can lead to serious health issues.
  • Stabilizing the human heartbeat is a significant area of research.
  • Existing methods may not adequately address complex cardiac dynamics.

Purpose of the Study:

  • To develop and evaluate a novel control strategy for stabilizing cardiac activity.
  • To enhance the adaptability and robustness of cardiac rhythm management.
  • To investigate the efficacy of a combined adaptive nonlinear disturbance compensator and deep reinforcement learning approach.

Main Methods:

  • Developed an adaptive nonlinear disturbance compensator (ANDC) strategy.
  • Employed a double deep reinforcement learning (DDRL) algorithm for adaptive controller calibration.
  • Constructed a dynamic heart model using the Markov Decision Process (MDP) framework.
  • Implemented a closed-loop system with ANDC for stability and disturbance mitigation, and DDRL for parameter refinement.
  • Assessed performance using normal and stochastic signals, and simulated pathological neural activity with multiple frequency components.

Main Results:

  • The ANDC-DDRL strategy demonstrated effective stabilization of cardiac rhythms.
  • The system showed robustness under normal, uncertain (stochastic signals), and pathological conditions.
  • Quantitative assessments (peak amplitude, signal energy, zero-crossing rate) confirmed improved cardiac model states.
  • The proposed method outperformed conventional baseline techniques in stabilizing cardiac activity.

Conclusions:

  • The ANDC-DDRL strategy provides an effective solution for stabilizing erratic cardiac rhythms.
  • This adaptive control approach offers improved performance compared to traditional methods.
  • The findings support the potential of advanced control algorithms in managing cardiovascular pathologies.