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.

The inverse problem of electrocardiography is ill-posed. Errors in the model such as signal noise can impact the accuracy of reconstructed cardiac electrical activity. It is currently not known how sensitive the inverse problem is to signal processing techniques. To evaluate this, experimental data from a Langendorff-perfused pig heart (n=1) suspended in a human-shaped torso-tank was used. Different signal processing methods were applied to torso potentials recorded from 128 electrodes embedded in the tank surface. Processing methods were divided into three categories i) high-frequency noise removal ii) baseline drift removal and iii) signal averaging, culminating in n=72 different signal sets. For each signal set, the inverse problem was solved and reconstructed signals were compared to those directly recorded by the sock around the heart. ECG signal processing methods had a dramatic effect on reconstruction accuracy. In particular, removal of baseline drift significantly impacts the magnitude of reconstructed electrograms, while the presence of high-frequency noise impacts the activation time derived from these signals (p<0.05).

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 torsotank 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 postreconstruction produced no differences compared to prereconstruction. 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 postreconstruction to ensure over-filtering does not occur.

Studying electrocardiography is intellectually challenging since it involves a myriad of theories and complex associations. For example, the electrocardiogram (ECG) is a non-invasive recording of the electrical activity of the heart and its interpretation requires an intricate understanding of how these electrical signals relate to cardiac mechanics (referred to as the electromechanical link). Furthermore, it is often diff icult to revert signals recorded from the body surface to the internal health of an organ. To help alleviate these challenges, the ECGSim software application was developed to allow visual learners to modifY transmembrane potentials on the myocardium and view how these changes affect the ECG at the body surface. ECGSim is based on a state of the art complex 'forward model' that can be used to aid understanding of how the electrical activity propagates from the myocardium to the epidermis. However, to maximize the uptake of ECGSim, this study quantifies and validates its usability using a series of metrics.

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