2025/10/27 by Avishka Herath, Malith Jayalath, Herath, Avishka +17
Computer Science · Engineering · #FOS: Electrical engineering #Signal Processing (eess.SP) #Systems and Control (eess.SY) #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2510.23819
published as The 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026), Toronto, Canada · This work is accepted for the IEEE EMBC 2026 Proceedings
arxiv created 2026/07/31 · arxiv updated 2026/08/04
Cardiac auscultation is an essential clinical skill, requiring excellent hearing to distinguish subtle differences in timing and pitch of heart sounds. However, diagnosing solely from these sounds is often challenging due to interference from surrounding noise, and the information may be limited. Most of the existing solutions that adaptively cancel external noise are either non-real-time or computationally intensive, making them unsuitable for implementation in a portable system. This work proposes an end-to-end system with a real-time adaptive noise cancellation pipeline integrated into a device that simultaneously acquires electrocardiogram (ECG) and phonocardiogram (PCG) signals. We employ a burst-adaptive normalized least mean square algorithm that adjusts its adaptation in response to high-energy, non-stationary hospital noise. The algorithm's performance was initially assessed using datasets with artificially induced noise. Subsequently, the complete end-to-end system was validated using real-world hospital recordings captured with the dual-modality device. For ECG and PCG signals recorded from the device in noisy hospital settings, the proposed system achieved signal-to-noise ratio improvements of 30.32 dB and 37.01 dB, respectively. Furthermore, complexity analysis confirms the pipeline's suitability for embedded implementation. These results demonstrate the system's effectiveness in enabling reliable and accessible cardiac screening in noisy hospital environments typical of resource-constrained settings.