An Open-Source Algorithm for Standardized Bullseye Visualization of High-Resolution Cardiac Ventricular Data: UNISYS

Stoks, Job; Nguyên, Uyên Châu; Peeters, Ronald; Volders, Paul G.A.; Cluitmans, Matthijs

doi:10.22489/cinc.2020.160

Cited by 4 publications

(6 citation statements)

References 3 publications

(5 reference statements)

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“…Our standardized bullseye visualization (UNISYS 13 ) enabled standardized comparisons between epicardial segments within and between subjects, but thereby omitted the location of 3D anatomic structures that may vary between subjects, such as the outflow tracts and coronary arteries. Although we distinguished between males and females, and young and old subjects, we did not account for other potential confounding factors, such as the menstrual cycle phase, autonomic tone or time of recording (all of which could have influenced activation and recovery duration, RR interval, ion-channel expression and/or arrhythmia susceptibility).…”

Section: Study Limitationsmentioning

confidence: 99%

Variant electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging

Stoks,

Patel,

van Rees

et al. 2024

Preprint

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Background and aims Although electrical activity of the normal human heart is well-characterized by the 12-lead electrocardiogram, detailed insights into within-subject and between-subject variations of ventricular activation and recovery by noninvasive electroanatomic mapping are lacking. We characterized human epicardial activation and recovery within and between normal subjects using noninvasive electrocardiographic imaging (ECGI) as a basis to better understand pathology. Methods Epicardial activation and recovery were assessed by ECGI in 22 normal subjects, 4 subjects with bundle branch block (BBB) and 4 with long-QT syndrome (LQTS). We compared characteristics between the ventricles (LV versus RV), sexes and age groups (<50y/≥50y (years)). Pearson's correlation coefficient (CC) was used for within-subject and between-subject comparisons. Results Age of normal subjects averaged 49+/-14 years, 6/22 were male, and no structural/electrical heart disease was present. Average activation time was longer in LV than in RV, but not different by sex or age. Electrical recovery was similar for the ventricles, but started earlier and was on average shorter in males than females. Median CCs of between-subject comparisons of the ECG signals, activation and recovery patterns were 0.61, 0.32 and 0.19, respectively. Within-subject beat-to-beat comparisons yielded higher CCs (0.98, 0.89 and 0.82, respectively). Activation and/or recovery patterns of patients with BBB or LQTS contrasted significantly with those found in the normal population. Conclusion Activation and recovery patterns vary profoundly between normal subjects, but are stable individually beat to beat, with a male preponderance to shorter recovery. Individual characterization by ECGI at baseline serves as reference to better understand the emergence, progression and treatment of electrical heart disease.

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Section: Study Limitationsmentioning

confidence: 99%

Variant electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging

Stoks,

Patel,

van Rees

et al. 2024

Preprint

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“…ATs and RTs are expressed relative to the start of QRS on the 12lead ECG. Our previously developed algorithm UNISYS [2] was used for standardized analysis of different segments of the ventricles. Epicardial bullseyes were separated into 20 segments, see Fig.…”

Section: Preprocessing and Reconstructionmentioning

confidence: 99%

Dynamics of Ventricular Electrophysiology Are Unmasked Through Noninvasive Electrocardiographic Imaging

Stoks

Rees

Nguyên

et al. 2021

2021 Computing in Cardiology (CinC)

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Dynamic variability of ventricular activation and recovery can be a physiological phenomenon, but is also known to increase susceptibility to arrhythmias. It has been extensively studied on the 12-lead electrocardiogram (ECG), but subtle (patho)physiological variations may be challenging to detect and localize due to the limited spatial resolution. Electrocardiographic imaging (ECGI) could be a useful noninvasive high-resolution mapping technique to investigate ventricular dynamics in more detail. Ventricular activation and recovery times (ATs and RTs) were examined using ECGI in 10 normal subjects. Zero-th order Tikhonov regularization was used in combination with a spatiotemporal estimation method to determine ATs and RTs. Dynamics were defined as standard deviations of ventricular ATs and RTs over three beats. Dynamics were higher for recovery than for activation during sinus rhythm, and significantly exaggerated after ventricular ectopy. Left ventricular areas were less dynamic than right ventricular areas. Since arrhythmias may arise due to an increase in ventricular dynamics in the diseased heart, these results provide an important basis for future research on ventricular dynamics and arrhythmias.

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“…Then following the torso normalization process, the 2D nBSPM was generated for each time instant of each individual beat. To generate corresponding 2D HSPMs, we start from the data at the mesh points underlying the BullsEye plots proposed in [3]. The polar coordinates were transformed back into Cartesian coordinates to obtain rectangular plots as in nBSPM, see Figure 4.…”

Section: Data Preparationmentioning

confidence: 99%

“…This is used as input to a neural network, so that the network can be sufficiently independent of a specific patient's geometry. With heart surface potential maps (HSPMs) expanded from BullsEye plots [3] as the output, we also build a 4-layer deep neural network.…”

Section: Introductionmentioning

confidence: 99%

ECGI with a Deep Neural Network and 2D Normalized Body Surface Potential Maps

Wang

Bonizzi

Karel

et al. 2021

2021 Computing in Cardiology (CinC)

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Electrocardiographic Imaging (ECGI) reconstructs heart surface potentials (HSPs) from body surface potentials (BSPs) using a patient-specific torso-heart geometry derived from CT or MRI. Potential inaccuracies in the estimate of the torso-heart geometry, and of the electrode positions on the body surface may limit the accuracy of the reconstructed HSPs. In this study, we aim at providing a proof-of-principle Deep Neural Network (DNN) which directly maps BSPs to HSPs, without the need to estimate a transfer matrix for the forward problem. In particular, we propose a torso normalization method that can turn the original torso geometry into a 2D image, and transform it into a normalized body surface potential map (nBSPM). In this paper, we did this for four dog geometries. A 4-layer back propagation (BP) neural network is built and trained with the nBSPMs as the input, and the pixel values of corresponding heart surface potential maps (HSPMs) as the output. The experiments show that the mean-squared error (MSE) on the validation data set decreases and converges to around 3.0. The value of the Pearson correlation coefficient between the original and reconstructed HSPMs on the test data set is larger than 0.92. This indicates that the proposed model is suitable for reconstructing HSPs from BSPs.

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An Open-Source Algorithm for Standardized Bullseye Visualization of High-Resolution Cardiac Ventricular Data: UNISYS

Cited by 4 publications

References 3 publications

Variant electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging

Variant electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging

Dynamics of Ventricular Electrophysiology Are Unmasked Through Noninvasive Electrocardiographic Imaging

ECGI with a Deep Neural Network and 2D Normalized Body Surface Potential Maps

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