Adaptive cardiac resynchronization therapy device based on spiking neurons architecture and reinforcement learning...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Cardiology

Background:

• Cardiac resynchronization therapy (CRT) devices aim to improve heart function in patients with heart failure.
• Current CRT devices often lack dynamic adaptation to individual patient hemodynamics.
• Optimizing atrioventricular (AV) and interventricular (VV) delays is crucial for effective CRT.

Purpose of the Study:

• To implement a silicon-based spiking neural network (NN) for adaptive CRT device control.
• To dynamically optimize AV and VV pacing intervals using real-time hemodynamic sensor data.
• To evaluate the efficacy of the adaptive NN-controlled CRT in improving cardiac output.

Main Methods:

• Developed a spiking NN architecture incorporating Hebbian and reinforcement learning for synaptic weight adaptation.
• Integrated hemodynamic sensors and intracardiac electrograms (IEGMs) to provide input to the NN.
• Controlled the adaptive NN coprocessor with a deterministic algorithm master controller.
• Simulated cardiac output under adaptive and non-adaptive CRT conditions.

Main Results:

• The adaptive CRT device demonstrated a 30% higher simulated cardiac output compared to non-adaptive devices.
• The spiking NN exhibited synaptic plasticity, correlating hemodynamic patterns with optimal AV and VV pacing intervals.
• A dynamic learning rate parameter reflected the NN's adaptation process.

Conclusions:

• The developed spiking NN architecture offers a promising approach for intelligent, adaptive CRT.
• Dynamic optimization of pacing parameters based on hemodynamic feedback can significantly enhance CRT efficacy.
• This technology has the potential to improve the quality of life for patients with congestive heart failure.