Tetralogy of Fallot (ToF) is a congenital structural heart disease. While early diagnosis and corrective surgery allow most patients to live normal lives, some patients slowly deteriorate. The current inability to quantify the deterioration and predict these events prompts a data driven approach. Laplacian Eigenmaps (LEs) are a dimensionality reduction technique that can be used to project multi-lead ECGs onto a lower dimensional space. This pilot study aims to evaluate the ability of LEs to characterize deterioration of ToF patients. A general LE model is constructed, based on the 12-lead ECG recordings of 20 healthy controls. A set of distance metrics are developed to quantify the overall changes between different ECG recordings within this LE model. Statistically significant differences between control and ToF subjects were observed for most of the distance metrics. The analysis of changes over time in ToF patients indicates a general trend of increased distance over time in all the metrics, which can be related to a worsening condition. This indicates the relevance of LEs in multi-lead ECG processing, particularly for deterioration analysis.@en

Electrical storm (ES) in ICD patients, defined as 3 or more appropriate ICD interventions within a time span of 24 hours, is a medical emergency associated with adverse outcome. However, it is debated if ES is only a marker of progressive near end-stage cardiac disease or an ar-rhythmogenic entity on its own. Better understanding and prediction are necessary to manage the burden of ES. The goal of this study is to explore the relation between the presence of fragmented QRS (fQRS) and the manifestation of electrical storm in patients with an ICD for ischemic heart disease. A balanced dataset of 100 patients was considered for this study, where 50 patients with ischemic heart disease and dilated cardiomyopathy present ES. 12-lead ECG signals were analyzed from 3 years before until the moment of ES, divided in 4 visits. The fQRS level in the 12-lead ECG data recorded in each visit was automatically quantified with a score between 0 and 1 for each lead. A Friedman test between the first and last visit for each of the groups showed a significant increase in the average level of fragmentation for the patients presenting ES, absent in the control group. This suggests that there is a trend towards deterioration in fQRS for patients manifesting ES with an ICD for ischemic heart disease.@en

The gold standard for the assessment of cardiac mass is cardiac magnetic resonance imaging (CMR). However, it is costly and requires specific expertise. Electrocardiographic (ECG) criteria could provide a low-cost solution, but have shown to be poorly correlated with LVM in athletes. We hypothesize that this poor correlation could be overcome by taking into account body measurements (length, weight) and composition (fat mass, lean mass and bone mass). The objective was to assess whether adding demographic (Demo) and/or Dual-energy X-ray absorptiometry (DXA) features could improve an ECG-based regression model for the estimation of LVM in athletes. 107 young competitive endurance athletes (19±2 years; 35 female) underwent a 12-lead ECG, a DXA scan and CMRI. We constructed four feature subsets: ECG, ECG+Demo, ECG+DXA and All. The best combination of features from each set, was used to build a Support Vector Machines regression model with 5 features. The ECG model performed significantly worse than all other models (R2 = 0.28 (0.17), RMSE = 34.33 (5.63) g). The best performing model was constructed with the entire feature set ((R2 = 0.67 (0.14), RMSE = 23.08 (4.42) g). These results suggest that an ECG based regression model for LVM prediction can be improved by adding demographic and/or body composition features.@en