Real-time 3D cardiac tissue implementation for arrhythmia detection and management

Area of Science:

• Computational biology
• Biomedical engineering
• Cardiovascular research

Background:

• Complex arrhythmias pose significant challenges in cardiac management.
• Accurate real-time simulation of cardiac electrical activity is crucial for understanding and treating arrhythmias.

Purpose of the Study:

• To present a high-performance, real-time hardware implementation for 3D cardiac tissue simulation.
• To develop a system for the detection and management of complex arrhythmias.
• To create a digital twin-inspired framework for autonomous therapeutic interventions.

Main Methods:

• Utilized the Aliev-Panfilov reaction-diffusion model for electrical wave propagation simulation.
• Developed a massively parallel architecture on a Xilinx Virtex-7 FPGA using multi-precision fixed-point arithmetic.
• Implemented a hardware-level supervisory layer for cardiac state classification.

Main Results:

• Achieved a full lattice update in 0.96 ms at 125 MHz, a 56-fold acceleration over biological real-time.
• Maintained high physiological fidelity with a Mean Squared Error (MSE) of 10 compared to 64-bit floating-point simulations.
• Successfully classified cardiac states into Periodic Spiral, Quasi-Periodic, and Spiral Turbulence.

Conclusions:

• The digital twin-inspired framework enables sub-millisecond, closed-loop therapeutic interventions.
• Facilitates autonomous management of life-threatening arrhythmic conditions.
• Demonstrated efficient hardware implementation with low power consumption (5.42 W).