QC-CNN: Highly Quantized Compressive CNN for Efficient Ventricular Arrhythmia Detection in Implantable Cardioverter Defibrillators

The Implantable Cardioverter Defibrillator (ICD) is a device designed to reduce the risk of Sudden Cardiac Death (SCD) by detecting life-threatening ventricular arrhythmias (VAs) through intracardiac electrograms (IEGMs). Ensuring accurate VA detection across diverse patient populations remains a challenge, as traditional ICDs either rely on manual tuning of patient-specific parameters or suffer from low detection accuracy. While machine learning models, particularly convolutional neural networks (CNNs), have demonstrated improved accuracy and flexibility, their large model size and high sampling frequency make them hard to be deployed in ICDs due to limited memory and energy capacity on ICDs. To address these constraints, we propose a compressive sensing (CS)-inspired CNN architecture that reduces model size by 50× and reduce the sample frequency by 25×. By reconstructing signals with fewer measurements and applying low-bit quantization, our model is optimized for efficient execution on analog domain frontend ADC. Experimental results show our model could use only 3% power consumption of the classic approach on ADC with no accuracy loss.