Short:VANet: An Intuitive Light-Weight Deep Learning Solution Towards Ventricular Arrhythmia Detection

Ventricular Arrhythmia (VA) is a leading cause of sudden cardiac death (SCD), which kills an average of 180,000 to 350,000 people annually, accounting for 15%–20% of all deaths. Furthermore, fewer than 6% of those who experience sudden cardiac arrest outside the hospital survive, compared to 24% of those who experience SCD inside a hospital. To aid in earlier detection and improve outcomes for out-of-hospital cardiac events, an automated passive detection system for these events could be used. Such automated detection would allow users to raise their self-awareness of potential cardiac risks in life-threatening situations. Diagnosis and detection of heart dysfunctions at early stages can help to prevent complications of a patient’s condition.

In this work, we propose VANet and design framework for ECG-related application, a small-scale deep learning-based real-time inference solution for VA detection. VANet achieves milliseconds scale inference speed on various platforms, including desktop CPUs, mobile devices, micro-controllers, and devices with constrained computation resources. It only requires a minimum of 13 kb of storage space and 34 kb of available run-time, making it small enough to be integrated into portable devices such as smartwatches and other Internet of Things (IoT) medical monitoring devices. VANet can trigger an alarm whenever it is necessary to alert someone with cardiac dysfunction.

VANet leverages optimization techniques, such as residual connections, and architecture designs, such as transformers and RNNs, to maximize neural network performance and minimize computational and storage costs. Our architecture achieved a 96.89% accuracy using multiple different ECG collection devices.