Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection

Abstract

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.

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

“…The magnitude of the cardiac interventricular septal (IVS) motion velocity from B-mode sequences was derived by applying the phase-to-phase deviation measure elaborated in [31] . For each subject, quiescence was identified from the velocity magnitude using a voting mechanism, which can be modeled as a linear function of heart rate [14] . Quiescence derived using the modeled linear function was considered as the baseline when comparing quiescence derived from ECG and SCG.…”

“…This paper builds upon our earlier work [14] where we developed an SCG-based quiescence detection and prediction method. In the SCG-based method, we focused on the frequency component (10-45 Hz) of SCG associated with cardiac sounds.…”

Recent Advances in Seismocardiography

“…Most research groups applied conventional band-pass filters to remove baseline wandering, body movements, and breathing artefacts from SCG signals [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93]. A few studies utilized or proposed more advanced noise removal techniques [64,76,88,[94][95][96].…”

“…More studies are needed that compare different filtering methods in clinical and ambulatory settings. [26,36,38,41,45,46,55,[58][59][60][61][62][63]67,71,75,76,[78][79][80]82,93] Adaptive filtering Motion artefact removal [88,95] Averaging theory Motion artefact removal [101] Comb filtering Removing respiration noise from radar signal [50] Empirical mode decomposition Baseline wandering, breathing and body movement artefact removal [76,94,95] Independent component analysis Motion artefact removal [102] Median filtering [96] Morphological filtering [95] Polynomial smoothing Motion artefact removal [103] Savitzky-Golay filtering Motion artefact removal [83,103] Wavelet denoising Segmentation of HSs and SCG [64,95,96] Wiener filtering [94] 2.…”

“…In addition to human studies on healthy populations, there were several studies that focused on the application of SCG in patients with cardiovascular disease. SCG signals were used for diagnosis and monitoring of different clinical conditions such as atrial fibrillation [47,48,68,70], atrial flutter [55], heart valve disease [37,44,80], coronary artery disease and ischemia [9,10,48], myocardial infarction [126], heart failure [75,78,82,100,119], structural heart disease [80], and heart stress testing [58].…”