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

• Published in: IEEE journal of translational engineering in health and medicine 2018-01, Vol.6, p.1-11
• Main Authors: Yao, Jingting, Tridandapani, S., Auffermann, W. F., Wick, C. A., Bhatti, P. T.
• Format: Article
• Language: English
• Subjects: Accelerometers; Angiography; Artificial neural networks; cardiac gating; cardiac quiescence; Computed tomography; computed tomography angiography; coronary angiography; Data acquisition; Diagnostic systems; Echocardiography; Electrocardiography; Evaluation; Heart; Heart rate; multimodal gating; Neural networks; Noise prediction; Radiation effects; Seismocardiography
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2018.2869141

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 ECGand SCG-based 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 traditional prospective gating method. Only one patient whose SCG was compromised by noise was more suitable for ECG-only-based prediction. On average, our fused ECGSCG-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 than ECG-onlyor SCG-only-based prediction. To assess the clinical value, the diagnostic quality of the CCTA reconstructed volumes from the quiescence derived from ECG-, SCGand 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.