Dataset Information

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

ABSTRACT: Measurements of the left ventricular (LV) pressure trace are rarely performed despite high clinical interest. We estimated the LV pressure trace for an individual heart by scaling the isovolumic, ejection and filling phases of a normalized, averaged LV pressure trace to the time-points of opening and closing of the aortic and mitral valves detected in the individual heart. We developed a signal processing algorithm that automatically detected the time-points of these valve events from the motion signal of a miniaturized accelerometer attached to the heart surface. Furthermore, the pressure trace was used in combination with measured displacement from the accelerometer to calculate the pressure-displacement loop area. The method was tested on data from 34 animals during different interventions. The accuracy of the accelerometer-detected valve events was very good with a median difference of 2 ms compared to valve events defined from hemodynamic reference recordings acquired simultaneously with the accelerometer. The average correlation coefficient between the estimated and measured LV pressure traces was r?=?0.98. Finally, the LV pressure-displacement loop areas calculated using the estimated and measured pressure traces showed very good correlation (r?=?0.98). Hence, the pressure-displacement loop area can be assessed solely from accelerometer recordings with very good accuracy.

SUBMITTER: Wajdan A

PROVIDER: S-EPMC7674430 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

Wajdan Ali A Krogh Magnus Reinsfelt MR Villegas-Martinez Manuel M Halvorsen Per Steinar PS Elle Ole Jakob OJ Remme Espen Wattenberg EW

Scientific reports 20201118 1

Measurements of the left ventricular (LV) pressure trace are rarely performed despite high clinical interest. We estimated the LV pressure trace for an individual heart by scaling the isovolumic, ejection and filling phases of a normalized, averaged LV pressure trace to the time-points of opening and closing of the aortic and mitral valves detected in the individual heart. We developed a signal processing algorithm that automatically detected the time-points of these valve events from the motion ...[more]

PMID: 33208784

Similar Datasets

Project description:?-Trace protein (BTP) and ?2-microglobulin (B2M) are novel glomerular filtration markers that have stronger associations with adverse outcomes than creatinine. Comparisons of BTP and B2M to creatinine and cystatin C are limited by the absence of rigorously developed glomerular filtration rate (GFR) estimating equations for the novel markers.Study of diagnostic test accuracy.Pooled database of 3 populations with chronic kidney disease (CKD) with mean measured GFR of 48 mL/min/1.73 m2 (N=3,551; MDRD [Modification of Diet in Renal Disease] Study, AASK [African American Study of Kidney Disease and Hypertension], and CRIC [Chronic Renal Insufficiency Cohort] Study).GFR estimated using creatinine, cystatin C, BTP, or B2M level.GFR measured as the urinary clearance of iothalamate.For BTP and B2M, coefficients for age, sex, and race were smaller than for creatinine and were similar or smaller than for cystatin C. For B2M, coefficients for sex, age, and race were smaller than for creatinine and were similar (age and race) or smaller (sex) than for cystatin C. The final equations with BTP (BTP, age, and sex) or B2M (B2M alone) were less accurate than either the CKD-EPI (CKD Epidemiology Collaboration) creatinine or cystatin C equations. The combined BTP-B2M equation (BTP and B2M alone) had similar accuracy to the CKD-EPI creatinine or cystatin C equation. The average of the BTP-B2M equation and the CKD-EPI creatinine-cystatin C equation was not more accurate than the CKD-EPI creatinine-cystatin C equation.No external validation population, study population was restricted to CKD, few participants older than 65 years, or nonblack nonwhite race.BTP and B2M are less influenced by age, sex, and race than creatinine and less influenced by race than cystatin C, but provide less accurate GFR estimates than the CKD-EPI creatinine and cystatin C equations. The CKD-EPI BTP and B2M equation provides a methodological advance for their study as filtration markers and in their associations with risk and adverse outcomes, but further study is required before clinical use.

Project description:Obtaining accurate displacement estimates along both axial (parallel to the acoustic beam) and lateral (perpendicular to the beam) directions is an important task for several clinical applications such as shear strain imaging, modulus reconstruction and temperature imaging, where a full description of the two or three-dimensional (2D/3D) deformation field is required. In this study we propose an improved speckle tracking algorithm where axial and lateral motion estimations are simultaneously performed to enhance motion tracking accuracy. More specifically, using conventional ultrasound echo data, this algorithm first finds an iso-contour in the vicinity of the peak correlation between two segments of the pre- and post-deformation ultrasound radiofrequency echo data. The algorithm then attempts to find the center of the iso-contour of the correlation function that corresponds to the unknown (sub-sample) motion vector between these two segments of echo data. This algorithm has been tested using computer-simulated data, studies with a tissue-mimicking phantom, and in vivo breast lesion data. Computer simulation results show that the method improves the accuracy of both lateral and axial tracking. Such improvements are more significant when the deformation is small or along the lateral direction. Results from the tissue-mimicking phantom study are consistent with findings observed in computer simulations. Using in vivo breast lesion data we found that, compared to the 2D quadratic subsample displacement estimation methods, higher quality axial strain and shear strain images (e.g. 18.6% improvement in contrast-to-noise ratio for shear strain images) can be obtained for large deformations (up to 5% frame-to-frame and 15% local strains) in a multi-compression technique. Our initial results demonstrated that this conceptually and computationally simple method could improve the image quality of ultrasound-based strain elastography with current clinical equipment.

Project description:BackgroundPseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets.ResultsThe simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets.ConclusionsWe introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable.

Dataset Information

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

Publications

Automatic detection of valve events by epicardial accelerometer allows estimation of the left ventricular pressure trace and pressure-displacement loop area.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure