Phonocardiogram Segmentation with Tiny Computing

The stethoscope is a daily used tool that allows medical doctors to diagnose common cardiovascular diseases by listening to heart sounds. However, dedicated medical training is required to operate it. Numerous machine learning techniques have been used in attempts to automate this process and have yielded highly accurate results. However, creating a low power, portable, economical, and accurate machine learning stethoscope calls for tiny processing of phonocardiograms i.e., heart sound digital processing to run within an embedded device. To address the need to deploy the solution within a constrained tiny device, we propose an 8-bit deep learning model with low embedded FLASH and RAM utilization of 126 KiB and 45 KiB respectively, which is optimized for inference on an off-the-shelf STM32H7 microcontroller with an inference time of 12 ms, in 126KiB FLASH and 45 KiB RAM being 91.65% accurate.