Diagnostic performance of the Selvester QRS scoring system in relation to clinical ECG assessment of patients with lateral myocardial infarction using cardiac magnetic resonance as reference standard

Semantic Segmentation of QRS complex in 12-Lead ECG Signals

In this work we present a method for semantic segmentation of the QRS complex in 12-lead ECG signals. The dataset used in the work was St Petersburg INCART 12-lead. The proposed method is comprised or three steps: 1. data preprocessing, where the binary vectors corresponding to the ground truth are obtained. In the ground truth vector, the samples corresponding to the QRS complex are identified as logical 1, while the other samples are identified as logical 0; 2. segmentation of the QRS complex, using autoencoders. Three different autoencoder architectures, with different number of kernels in convolutions layers are evaluated; 3. post-processing filters to remove noise, desgined using heuristic rules. The result obtained for detecting the QRS complex, an F1-score over 99.90%, is the state-of-the-art and surpasses the performance obtained with other methods previously published in the literature.

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type

Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F₁ scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software.