Computational Analysis of a Multi-Layered Skin and Cardiac Pacemaker Model Based on Neural Network Approach.

Area of Science:

• Biomedical Engineering
• Electromagnetics
• Computational Modeling

Background:

• Pacemakers are crucial for cardiac function.
• Exposure to electromagnetic fields (EMF) from wireless technologies poses potential risks to implanted medical devices.
• Understanding EMF interaction with pacemakers is vital for patient safety.

Purpose of the Study:

• To investigate the impact of electromagnetic field exposure on cardiac pacemakers.
• To determine how subcutaneous tissue thickness, specifically the hypodermis, affects pacemaker performance under EMF.
• To develop a predictive model for EMF interference with pacemakers based on tissue depth.

Main Methods:

• Computational mathematical analysis and modeling of a pacemaker under the skin.
• Simulation of electromagnetic field exposure using PIFA and tuned dipole antennas.
• Implementation of the Finite Integration Technique (FIT) in CST Microwave Studio.
• Development of a neural network trained on simulated data relating hypodermis thickness to electric field strength.

Main Results:

• A mathematical model was established to describe the relationship between hypodermis thickness and electric field exposure.
• A multilayer feedforward neural network was successfully trained using simulated data.
• The neural network demonstrates the dependence of electromagnetic field strength on hypodermis thickness.

Conclusions:

• The study provides a novel computational approach to assess EMF interference with pacemakers.
• The developed mathematical model and neural network can predict pacemaker response based on anatomical variations.
• Findings contribute to enhancing the safety of pacemaker patients in environments with wireless technology.