Feasibility study of a 3D camera to reduce electrode repositioning errors during longitudinal ECG acquisition.

INTRODUCTION: Longitudinal monitoring of sometimes subtle waveform changes of the 12­lead electrocardiogram (ECG) is complicated by patient-specific and technical factors, such as the inaccuracy of electrode repositioning. This feasibility study uses a 3D camera to reduce electrode repositioning errors, reduce ECG waveform variability and enable detailed longitudinal ECG monitoring. METHODS: Per subject, three clinical ECGs were obtained during routine clinical follow-up. Additionally, two ECGs were recorded guided by two 3D cameras, which were used to capture the precordial electrode locations and direct electrode repositioning. ECG waveforms and parameters were quantitatively compared between 3D camera guided ECGs and clinical ECGs. Euclidian distances between original and repositioned precordial electrodes from 3D guided ECGs were measured. RESULTS: Twenty subjects (mean age 65.1 ± 8.2 years, 35% females) were included. The ECG waveform variation between routine ECGs was significantly higher compared to 3D guided ECGs, for both the QRS complex (correlation coefficient = 0.90 vs 0.98, p < 0.001) and the STT segment (correlation coefficient = 0.88 vs. 0.96, p < 0.001). QTc interval variation was reduced for 3D camera guided ECGs compared to routine clinical ECGs (5.6 ms vs. 9.6 ms, p = 0.030). The median distance between 3D guided repositioned electrodes was 10.0 [6.4-15.2] mm, and did differ between males and females (p = 0.076). CONCLUSIONS: 3D guided repositioning of precordial electrodes resulted in, a low repositioning error, higher agreement between waveforms of consecutive ECGs and a reduction of QTc variation. These findings suggest that longitudinal monitoring of disease progression using 12­lead ECG waveforms is feasible in clinical practice.

The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions.

GOAL: To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data. METHODS: Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded. RESULTS: HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05). CONCLUSION: BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.

A Unified Pipeline for ECG Imaging Testing.

The Consortium for ECG Imaging (CEI) has formed several collaborative projects to evaluate and improve technical aspects of Electrocardiographic Imaging (ECGI), but these efforts are not yet implemented into an integrated software framework. We developed a framework to unify the multiple techniques and stages of ECGI into one pipeline. This framework merges existing open source packages: SCIRun, a problem solving environment; the Forward/Inverse toolkit, a series of SCIRun modules for ECGI; and PFEIFER, a cardiac signal pre-processing tool. The Unified ECGI Toolkit (UETK), combined with the EDGAR dataset, allows users to test and validate a vast array of parameters within each stage of the ECGI pipeline. We expect that this unified tool will help introduce new researchers to ECGI, facilitate interaction between the various groups working on ECGI, and establish a common approach for researchers to test and validate their ECGI techniques.

Torso geometry reconstruction and body surface electrode localization using three-dimensional photography.

We conducted a prospective clinical study (n=14; 29% female) to assess the accuracy of a three-dimensional (3D) photography-based method of torso geometry reconstruction and body surface electrodes localization. The position of 74 body surface electrocardiographic (ECG) electrodes (diameter 5mm) was defined by two methods: 3D photography, and CT (marker diameter 2mm) or MRI (marker size 10×20mm) imaging. Bland-Altman analysis showed good agreement in X (bias -2.5 [95% limits of agreement (LoA) -19.5 to 14.3] mm), Y (bias -0.1 [95% LoA -14.1 to 13.9] mm), and Z coordinates (bias -0.8 [95% LoA -15.6 to 14.2] mm), as defined by the CT/MRI imaging, and 3D photography. The average Hausdorff distance between the two torso geometry reconstructions was 11.17±3.05mm. Thus, accurate torso geometry reconstruction using 3D photography is feasible. Body surface ECG electrodes coordinates as defined by the CT/MRI imaging, and 3D photography, are in good agreement.