SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions
This enables practical, low-cost, and automated cardiac-rhythm monitoring for scalable cardiovascular assessment in field conditions, though it is incremental as it builds on existing deep-learning methods.
The paper tackled the problem of detecting rhythmic spectrum disorders from noisy seismocardiography signals collected via consumer smartphones, achieving consistently high accuracy and robustness across various device types and unsupervised conditions.
Aortic valve opening (AO) events are crucial for detecting frequency and rhythm disorders, especially in real-world settings where seismocardiography (SCG) signals collected via consumer smartphones are subject to noise, motion artifacts, and variability caused by device heterogeneity. In this work, we present a robust deep-learning framework for SCG segmentation and rhythm analysis using accelerometer recordings obtained with consumer smartphones. We develop an enhanced U-Net v3 architecture that integrates multi-scale convolutions, residual connections, and attention gates, enabling reliable segmentation of noisy SCG signals. A dedicated post-processing pipeline converts probability masks into precise AO timestamps, whereas a novel adaptive 3D-to-1D projection method ensures robustness to arbitrary smartphone orientation. Experimental results demonstrate that the proposed method achieves consistently high accuracy and robustness across various device types and unsupervised data-collection conditions. Our approach enables practical, low-cost, and automated cardiac-rhythm monitoring using everyday mobile devices, paving the way for scalable, field-deployable cardiovascular assessment and future multimodal diagnostic systems.