A Deep Learning Approach for Ventricular Arrhythmias Classification using Microcontroller

Intra-Cardiac Electrogram (IEGM) is widely used to identify life-threatening ventricular arrhythmias in medical devices to prevent sudden cardiac death, e.g., Implantable Cardioverter Defibrillator (ICD). In this paper, we present and explore the development of a machine learning approach for the detection of life-threatening Heart Arrhythmias through IEGM Data from an ICD Device. This work is facilitated by the design and analysis of 2 Convolutional Neural Network (CNN), 1D and 2D CNNs, that perform inference on a Low Power STM Nucleo-32 MCU. Multiple microcontroller software platforms are utilized to construct and deploy the trained models onto the MCU platform for inference measurements. The experimental analysis consists of minimizing Average Inference time and onboard Memory Occupation while maximizing the accuracy of the models. We profile the memory occupation and inference time for different CNN kernels. We develop a 1D CNN structure with a 26.20 ms Average Inference out of 10 measurements taken by the MCU platform. Model Weights in Flash Memory Occupied 5.99 KiB and Model Activations in SRAM (Static Random Access Memory) measure 5.00 KiB. The 1D CNN achieves a Fβ score of 97.8. The 2D CNN Model achieves 11.00 ms of inference, 3.05 KiB of Flash, and 8.09 KiB of SRAM. The 2D CNN achieves a Fβ score of 95.15. Our code is publicly available at https://github.com/Zhoushanglin100/TinyML-HuskyCSDeepical.