Artificial intelligence algorithms are being adopted to analyze medical data, promising faster interpretation to support doctors' diagnostics. The next frontier is to bring these powerful algorithms to implantable medical devices. Herein, a closed-loop solution is proposed, where a cellular neural network is used to detect abnormal wavefronts and wavebrakes in cardiac signals recorded in human tissue is trained to achieve >96% accuracy, >92% precision, >99% specificity, and >93% sensitivity, when floating point precision weights are assumed. Unfortunately, the current hardware technologies for floating point precision are too bulky or energy intensive for compact standalone applications in medical implants. Emerging device technologies, such as memristors, can provide the compact and energy-efficient hardware fabric to support these efforts and can be reliably embedded with existing sensor and actuator platforms in implantable devices. A distributed design that considers the hardware limitations in terms of overhead and limited bit precision is also discussed. The proposed distributed solution can be easily adapted to other medical technologies that require compact and efficient computing, like wearable devices and lab-on-chip platforms.
Keywords: arrhythmia; cardiac implants; cellular neural networks; distributed computing; machine learning; memristors; resistive random access memory; wavefronts.