arXiv:2511.00966v2 Announce Type: replace Abstract: Early and reliable detection of heart murmurs is essential for the timely diagnosis of cardiovascular diseases, yet traditional auscultation remains subjective and dependent on expert interpretation. This work investigates an artificial intelligence (AI)-based approach for heart murmur detection using the CirCor Heart Sound dataset, with a particular focus on deployment in low-cost and resource-constrained embedded platforms. A lightweight convolutional neural network (CNN) is developed using spectrogram features extracted from heart sound recordings. The complete processing pipeline, from feature extraction to final prediction, is implemented and validated on an STM32F401 microcontroller (MCU). To enable real-time execution within the limited hardware resources of the target platform, memory- and latency-oriented optimizations together with fixed-point processing are employed. Experimental results demonstrate that accurate murmur detection can be achieved despite the severe computational constraints of the embedded device. The proposed system achieves a patient-level accuracy of 92.5% and an F1-score above 82.5%, while using far fewer parameters than conventional high-resource deep-learning approaches. The embedded implementation exhibits only marginal performance degradation, with performance drops below 2 percentage points compared to the original full-precision implementation. These findings demonstrate the feasibility of real-time AI-based heart murmur screening on low-cost microcontrollers. By reducing hardware requirements while preserving predictive performance, the proposed approach can facilitate the development of portable, affordable, and widely deployable diagnostic tools for remote and resource-limited healthcare settings.