WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis.

Introduction: Representation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream tasks. It was hypothesised that wave segmentation would be a useful form of electrocardiogram (ECG) representation learning.

The effect of confounding data features on a deep learning algorithm to predict complete coronary occlusion in a retrospective observational setting.

Aims: Deep learning (DL) has emerged in recent years as an effective technique in automated ECG analysis.

Methods And Results: A retrospective, observational study was designed to assess the feasibility of detecting induced coronary artery occlusion in human subjects earlier than experienced cardiologists using a DL algorithm. A deep convolutional neural network was trained using data from the STAFF III database.