Joint Alignment of Multivariate Quasi-Periodic Functional Data Using Deep Learning

The joint alignment of multivariate functional data plays an important role in various fields such as signal processing, neuroscience and medicine, including the statistical analysis of data from wearable devices. Traditional methods often ignore the phase variability and instead focus on the variability in the observed amplitude. We present a novel method for joint alignment of multivariate quasi-periodic functions using deep neural networks, decomposing, but retaining all the information in the data by preserving both phase and amplitude variability. Our proposed neural network uses a special activation of the output that builds on the unit simplex transformation, and we utilize a loss function based on the Fisher-Rao metric to train our model. Furthermore, our method is unsupervised and can provide an optimal common template function as well as subject-specific templates. We demonstrate our method on two simulated datasets and one real example, comprising data from 12-lead 10s electrocardiogram recordings.Comment: 28 pages, 6 figure

Pericardial fat volume as a predictor of atherosclerosis, stenosis severity and plaque composition in symptomatic and asymptomatic populations - a cardiac CT angiography study

Introduction: Coronary CT angiography (CCTA) can provide comprehensive information of the coronary arteries, including the identification of coronary plaques, determination of stenosis severity and morphological characterization of plaques with regard to the presence or absence of calcification – all of which have been linked to the pathogenesis of coronary artery disease (CAD). It is also possible to calculate the pericardial fat volume (PFV) using CCTA; however the relationship of PFV to CAD has been controversial. The aim of this study was to evaluate the relationship between PFV and these indicators of CAD in asymptomatic and symptomatic population-based cohorts. Method and Results: The CCTA scans of asymptomatic (383) people and symptomatic (549) patients were examined. The symptomatic patients were part of the “Cardiac cT in the treatment of acute Chest pain” (CATCH) trial and were admitted on suspicion of acute coronary syndrome with a normal electrocardiogram and troponins. The asymptomatic cohort was taken randomly from the Copenhagen General Population Study. Measurements were made on the PFV, stenosis grading, plaque characteristics, and coronary artery calcium (CAC) scores. Relations between PFV and the indicators of CAD were assessed using logistic regression. In the symptomatic population, 59% of the patients had atherosclerosis present, compared to 73% in the asymptomatic population. Symptomatic patients with atherosclerosis had a significantly higher mean PFV (142.96 mL) compared to those without (111.22 mL, P<0.001). A significant difference was also detected in the asymptomatic population (199.32 mL vs. 171.5 mL, P=0.079). When adjusting for age, gender, and BMI, a larger PFV was significantly associated with atherosclerosis in symptomatic patients (P=0.044), but not asymptomatic patients (P=0.358). PFV was not a predictor of significant coronary artery stenosis (≥70%) in either population, although in symptomatic patients, increased PFV was independently associated with the presence of noncalcified plaques (P=0.032). There was no relationship between PFV and CAC scores in symptomatic or asymptomatic populations (P=0.403 and P=0.292 respectively). Conclusion: These findings support the hypothesis that PFV contributes to the presence of coronary atherosclerosis and also can indicate the presence of non-calcified plaques, but only for symptomatic patients. However, PFV cannot predict the degree of stenosis.This research was supported by the Undergraduate Research Opportunities Program (UROP)

Guiding 3D U-nets with signed distance fields for creating 3D models from images

Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary labelmaps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.Comment: MIDL 2019 [arXiv:1907.08612

Respiratory influence on left atrial volume calculation with 3D-echocardiography

BACKGROUND: Left atrial volume (LAV) estimation with 3D echocardiography has been shown to be more accurate than 2D volume calculation. However, little is known about the possible effect of respiratory movements on the accuracy of the measurement. METHODS: 100 consecutive patients admitted with chest pain were examined with 3D echocardiography and LAV was quantified during inspiratory breath hold, expiratory breath hold and during free breathing. RESULTS: Of the 100 patients, only 65 had an echocardiographic window that allowed for 3D echocardiography in the entire respiratory cycle. Mean atrial end diastolic volume was 45.4 ± 14.5 during inspiratory breath hold, 46.4 ± 14.8 during expiratory breath hold and 45.6 ± 14.3 during free respiration. Mean end systolic volume was 17.6 ± 7.8 during inspiratory breath hold, 18.8 ± 8.0 during expiratory breath hold and 18.3 ± 8.0 during free respiration. No significant differences were seen in any of the measured parameters. CONCLUSIONS: The present study adds to the feasibility of 3D LAV quantitation. LAV estimation by 3D echocardiography may be performed during either end-expiratory or end-inspiratory breath-hold without any significant difference in the calculated volume. Also, the LAV estimation may be performed during free breathing

De novo electrocardiographic abnormalities in persons living with HIV

Abstract Persons living with HIV (PLWH) may have increased incidence of cardiovascular events and longer QTc intervals than uninfected persons. We aimed to investigate the incidence and risk factors of de novo major electrocardiogram (ECG) abnormalities and QTc prolongation in well-treated PLWH. We included virologically suppressed PLWH without major ECG abnormalities, who attended the 2-year follow-up in the Copenhagen comorbidity in HIV infection (COCOMO) study. ECGs were categorized according to Minnesota Code Manual. We defined de novo major ECG abnormalities as new major Minnesota Code Manual abnormalities. Prolonged QTc was defined as QTc > 460 ms in females and QTc > 450 ms in males. Of 667 PLWH without major ECG abnormalities at baseline, 34 (5%) developed de novo major ECG abnormalities after a median of 2.3 years. After adjustment, age (RR: 1.57 [1.08–2.28] per decade older), being underweight (RR: 5.79 [1.70–19.71]), current smoking (RR: 2.34 [1.06–5.16]), diabetes (RR: 3.89 [1.72–8.80]) and protease inhibitor use (RR: 2.45 [1.27–4.74) were associated with higher risk of getting de novo major ECG abnormalities. Of PLWH without prolonged QTc at baseline, only 11 (1.6%) participants developed de novo prolonged QTc. Five percent of well-treated PLWH acquired de novo major ECG abnormalities and protease inhibitor use was associated with more than twice the risk of de novo major ECG abnormalities. De novo prolonged QTc was rare and did not seem to constitute a problem in well-treated PLWH