Ultra-Efficient Compressed Phonocardiogram Classification on a Custom Embedded Neural Accelerator

Real-time phonocardiogram analysis on embedded devices is a key enabler for scalable and accessible cardiovascular diagnostics, particularly considering portable systems designed for low-income countries. This work introduces a combination of compressive sensing and deep learning to build portable, efficient and effective diagnostic tools for widespread cardiac screenings. The proposed classification framework is tailored for deployment on ultra-low power STM32 microcontrollers equipped with the novel Neural-ART accelerator. Experimental evaluation on the CirCor Digiscope Phonocardiogram dataset demonstrates that even with a compression ratio exceeding 100×, a classification model can achieve up to 97.3% F1-score. A similar level of performance was obtained on the PhysioNet 2016 dataset, which was used to assess the robustness and generalization capability of the developed architectures. Compared to the state-of-the-art, our final edge solution achieves 94.3% accuracy, an inference time of 18.7 ms and an energy requirement of just 1.51 mJ per input window of 4,096 samples, confirming its suitability for real-time, energy-constrained medical applications.