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Area of Science:

  • Computational electrophysiology
  • Artificial intelligence in healthcare
  • Biomedical signal processing

Background:

  • Current AI for arrhythmia classification relies on ECG data but often overlooks cardiac electrophysiology principles.
  • A finite element model (FEM) based on the Hodgkin-Huxley (HH) model is crucial for simulating cardiac electrical activity.

Purpose of the Study:

  • To develop an AI-driven arrhythmia classification model integrating cardiac electrophysiology with advanced signal processing.
  • To enhance ECG signal decomposition and feature extraction for improved classification accuracy.

Main Methods:

  • Simulated cardiac arrhythmias using a Hodgkin-Huxley (HH) based finite element model (FEM) to generate synthetic ECG signals.
  • Developed a multi-objective crayfish optimization algorithm (MOCOA)-Variational Mode Decomposition (VMD) technique for optimizing ECG signal decomposition.
  • Constructed a deep VMD-attention network, optimized with Bayesian optimization (TPE), for arrhythmia classification.

Main Results:

  • The MOCOA-VMD model achieved 94.46% accuracy, surpassing EEMD, VMD, CNN, and LSTM models.
  • The deep attention model, fine-tuned with Bayesian optimization, reached a peak accuracy of 96.11%.
  • The model demonstrated robustness and generalizability on the MIT-BIH arrhythmia database.

Conclusions:

  • The proposed deep VMD-attention modeling and classification strategy offers a promising AI-driven approach for arrhythmia detection.
  • Integrating mathematical cardiac models with advanced signal processing significantly improves classification performance.
  • This methodology holds potential for broader applications in biomedical signal processing.