Characterisation of atrial flutter variants based on the analysis of spatial vectorcardiographic trajectory from standard ECG

After atrial fibrillation, atrial flutter is the most common atrial tachyarrhythmia. Its diagnosis relies on the twelve lead electrocadriogram analysis of the distinctive waves in several leads. Nonetheless, the accurate identification of the type of atrial flutter still requires an invasive procedure. The maneuver for healing atrial flutter consists on ablating a section of the anatomy of the atria, to stop the macroreentrant circuit to keep happening, allowing the signal to travel to the ventricles in stead of staying at the atria. The region to ablate directly depends on the place at which the macroreentrant circuit is located, which at the same time depends on the type of atrial flutter. Being able to noninvasively detect the atrial flutter variant would produce a great advantage when healing this illness. The hypothesis stated at this dissertation is based on the slow conduction regions as the key factor to distinguish the atrial flutter class. This and unveiling further relations between cardiac illnesses and their signal’s alter ego are the purpose of this research project. With such aim, different methods are developed based on the vectorcardiographic representation of electrocardiograms from patients suffering from different atrial flutter types. These methods consist on the characterisation of vectorcardiographic signals from different standpoints. Besides, a mathematical model is implemented to create a large database with synthetic vectorcardiographic signals allowing to test the validity of the utilised methods. The results prove the importance of slow regions in the vectorcardiographic representation of the patient’s signals to characterise the atrial flutter type non-invasively. Furthermore, the analysis of the outcome of the different methods reveal a wide variety of features relating characteristics of the vectorcardiographic signal to the anatomy and physiology of this cardiac disease. Hence, not only results supporting the hypothesis were successful (taking into account some limitations), but also a variegated assortment of results unmasked remarkable relations among the vectorcardiographic signal and the characteristics of the atrial flutter disease.Ingeniería Biomédic

Quantifying Spasticity: A Review

A precise method to measure spasticity is fundamental in improving the quality of life of spastic patients. The measurement methods that exist for spasticity have long been considered scarce and inadequate, which can partly be explained by a lack of consensus in the definition of spasticity. Spasticity quantification methods can be roughly classified according to whether they are based on neurophysiological or biomechanical mechanisms, clinical scales, or imaging techniques. This article reviews methods from all classes and further discusses instrumentation, dimensionality, and EMG onset detection methods. The objective of this article is to provide a review on spasticity measurement methods used to this day in an effort to contribute to the advancement of both the quantification and treatment of spasticity

Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone

Background and objectives: Macroreentrant atrial tachyarrhythmias (MRATs) can be caused by different reentrant circuits. The treatment for each MRAT type may require ablation at different sites, either at the right or left atria. Unfortunately, the reentrant circuit that drives the arrhythmia cannot be ascertained previous to the electrophysiological intervention. Methods: A noninvasive approach based on the comparison of atrial vectorcardiogram (VCG) loops is proposed. An archetype for each group was created, which served as a reference to measure the similarity between loops. Methods were tested in a variety of simulations and real data obtained from the most common right (peritricuspid) and left (perimitral) macroreentrant circuits, each divided into clockwise and counterclockwise subgroups. Adenosine was administered to patients to induce transient AV block, allowing the recording of the atrial signal without the interference of ventricular signals. From the vectorcardiogram, we measured intrapatient loop consistence, similarity of the pathway to archetypes, characterisation of slow velocity regions and pathway complexity. Results: Results show a considerably higher similarity with the loop of its corresponding archetype, in both simulations and real data. We found the capacity of the vectorcardiogram to reflect a slow velocity region, consistent with the mechanisms of MRAT, and the role that it plays in the characterisation of the reentrant circuit. The intra-patient loop consistence was over 0.85 for all clinical cases while the similarity of the pathway to archetypes was found to be 0.85 ± 0.03, 0.95 ± 0.03, 0.87 ± 0.04 and 0.91 ± 0.02 for the different MRAT types (and p<0.02 for 3 of the 4 groups), and pathway complexity also allowed to discriminate among cases (with p<0.05). Conclusions: We conclude that the presented methodology allows us to differentiate between the most common forms of right and left MRATs and predict the existence and location of a slow conduction zone. This approach may be useful in planning ablation procedures in advance.ISSN:0169-2607ISSN:1872-756

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

Background: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. Methods: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. Findings: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755–0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642–0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867–0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. Interpretation: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council ( NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillatorResearch in context

Summary: Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)