An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks

October 10, 2018

Jingting Yao, Srini Tridandapani, William F. Auffermann, Carson A. Wick, Pamela Bhatti

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework

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 (ANN) that adaptively fuses individual ECG- and SCGbased quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence prediction for each cardiac cycle. This framework was tested on seven healthy subjects (age: 22–48; m/f: 4/3) and eleven cardiac patients (age: 31–78; m/f: 6/5). Seventeen out of 18 benefited from the fusion-based prediction as compared to the ECG-only-based prediction, the current gating standard. Only one patient whose SCG was compromised by noise was more suitable for ECG-only-based prediction. On average, our fused ECG-SCG-based method improves cardiac quiescence prediction by 47% over ECG-only-based method; with both compared against the gold standard, B-mode echocardiography. Fusion-based prediction is also more resistant to heart rate variability and associated error than ECG-only- or SCG-only-based prediction. To assess the clinical value, the diagnostic quality of the CCTA reconstructed volumes from the quiescence derived from ECG-, SCG- and fusion-based predictions were graded by a board-certified radiologist using a Likert response format. Grading results indicated the fusion-based prediction improved diagnostic quality. ECG may be a sub-optimal modality for quiescence prediction and can be enhanced by the multimodal framework. The combination of ECG and SCG signals for quiescence prediction bears promise for a more personalized and reliable approach than ECG-only-based method to predict cardiac quiescence for prospective CCTA gating.

READ FULL ARTICLE ON IEEE XPLORE

Login

New Here? Sign Up

Looking for increased exposure in the field of biomedical engineering? EMBS offers journals, conferences and a community for biomedical engineers. Membership includes PULSE Magazine.

Join EMBS