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. 2019 Sep 13:7:1900404.
doi: 10.1109/JTEHM.2019.2923353. eCollection 2019.

Near Real-Time Implementation of An Adaptive Seismocardiography - ECG Multimodal Framework for Cardiac Gating

J Yao  1 , S Tridandapani  2 , P T Bhatti  1
Affiliations

Near Real-Time Implementation of An Adaptive Seismocardiography - ECG Multimodal Framework for Cardiac Gating

J Yao et al. IEEE J Transl Eng Health Med. .

Abstract

Objective: Accurate gating for data acquisition of computed tomography (CT) is crucial to obtaining high quality images for diagnosing cardiovascular diseases. To illustrate the feasibility of an optimized cardiac gating strategy, we present a near real-time implementation based on fusing seismocardiography (SCG) and ECG. Methods: The implementation was achieved via integrating commercial hardware and software platforms. Testing was performed on five healthy subjects (age: 24-27; m/f: 4/1) and three cardiac patients (age: 41-71; m/f: 2/1), and compared with baseline quiescence derived from echocardiography. Results: The average latency introduced by computerized processing was 5.1 ms, well within a 100 ms tolerance bounded by data accumulation time for quiescence prediction. The average prediction error associated with conventional ECG-only versus SCG-ECG-based method over all subjects were 59.58 ms and 27.24 ms, respectively. Discussion: The results demonstrate that the multimodal framework can achieve improved quiescence prediction accuracy over the ECG-only-based method in near real-time.

Keywords: Cardiac gating; cardiac quiescence; computed tomography angiography; echocardiography; electrocardiography; multimodal gating; real-time implementation; seismocardiography.

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Figures

FIGURE 1.
FIGURE 1.
Overall strategy of the multimodal gating method. The SCG- and ECG-SCG-based prediction methods were developed to demonstrate the efficacy of cardiac-motion-based signal in quiescence prediction. The near real-time implementation evaluates the feasibility of the developed methods. The ultimate goal is investigating the potential of multimodal gating strategy in clinical practice.
FIGURE 2.
FIGURE 2.
Devices used in this work. The processing is based on MATLAB 2017 running on a Hewlett-Packard computer with an Intel 4-core. The technical details of the individual device components can be found in Table 1 of the supplementary document.
FIGURE 3.
FIGURE 3.
Breakdown of stages of (a) ECG and (b) SCG signals transmission and processing. The latency introduced by each stage i is denoted as formula image for i = 1, 2, 3, 4.
See this image and copyright information in PMC

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