Embedded Neural Networks for Edge Arrhythmia Detection

Running convolutional inference directly on wearable microcontrollers to trigger clinical alarms.

Integer Quantization and Memory Footprint

Deploying 1D-CNNs within 64 KB of RAM requires pruning low-weight synapses and quantizing weights to 8-bit integers, reducing power draw by 85%.

High Clinical Sensitivity and Specificity

The embedded model achieves over 99.1% sensitivity for ventricular premature contractions (VPCs) and atrial fibrillation, conforming to ANSI/AAMI EC57 standards.

Inference cycle completion in under 12 milliseconds at 32 MHz clock
Eliminates constant cloud streaming by evaluating waveforms locally
Continuous background learning with physician feedback fine-tuning

Health-Domain-Portfolio Erkunden