Open AccessSeismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection
Author(s) -
Jingting Yao,
Srini Tridandapani,
Carson A. Wick,
Pamela T. Bhatti
Publication year - 2017
Publication title -
ieee journal of translational engineering in health and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.653
H-Index - 24
ISSN - 2168-2372
DOI - 10.1109/jtehm.2017.2708100
Subject(s) - bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , signal processing and analysis , robotics and control systems , general topics for engineers
To more accurately trigger cardiac computed tomography angiography (CTA) than electrocardiography (ECG) alone, a sub-system is proposed as an intermediate step toward fusing ECG with seismocardiography (SCG). Accurate prediction of quiescent phases is crucial to prospectively gating CTA, which is susceptible to cardiac motion and, thus, can affect the diagnostic quality of images. The key innovation of this sub-system is that it identifies the SCG waveform corresponding to heart sounds and determines their phases within the cardiac cycles. Furthermore, this relationship is modeled as a linear function with respect to heart rate. For this paper, B-mode echocardiography is used as the gold standard for identifying the quiescent phases. We analyzed synchronous ECG, SCG, and echocardiography data acquired from seven healthy subjects (mean age: 31; age range: 22-48; males: 4) and 11 cardiac patients (mean age: 56; age range: 31-78; males: 6). On average, the proposed algorithm was able to successfully identify 79% of the SCG waveforms in systole and 68% in diastole. The simulated results show that SCG-based prediction produced less average phase error than that of ECG. It was found that the accuracy of ECG-based gating is more susceptible to increases in heart rate variability, while SCG-based gating is susceptible to high cycle to cycle variability in morphology. This pilot work of prediction using SCG waveforms enriches the framework of a comprehensive system with multiple modalities that could potentially, in real time, improve the image quality of CTA.