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An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks
2018
IEEE Journal of Translational Engineering in Health and Medicine
To more accurately trigger data acquisition and reduce radiation exposure of coronary computed tomography angiography (CCTA), a multimodal framework utilizing both electrocardiography (ECG) and seismocardiography (SCG) for CCTA prospective gating is presented. Relying upon a three-layer artificial neural network that adaptively fuses individual ECG- and SCG-based quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence prediction for each cardiac cycle.
doi:10.1109/jtehm.2018.2869141
pmid:30405976
pmcid:PMC6204924
fatcat:prk7gve2kjas7hekigqknpk6ie