IntroductionRepresentation 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.In addition to reducing labelled data requirements, segmentation masks may provide a mechanism for explainable AI.
This study details the development and evaluation of a Wave Segmentation Pretraining (WaSP) application.Materials and MethodsPretraining: A non-AI-based ECG signal and image simulator was developed to generate ECGs and wave segmentation masks.U-Net models were trained to segment waves from synthetic ECGs.Dataset: The raw sample files from the PTB-XL dataset were downloaded.
Each ECG was also st?vsugerposer aeg plotted into an image.Fine-tuning and evaluation: A hold-out approach was used with a 60:20:20 training/validation/test set split.The encoder portions of the U-Net models were fine-tuned to classify PTB-XL ECGs for two tasks: sinus rhythm (SR) vs atrial fibrillation (AF), and myocardial infarction (MI) vs normal ECGs.The fine-tuning was repeated without pretraining.
Results were compared.Explainable AI: an example pipeline combining AI-derived segmentation masks and a rule-based AF detector was developed and evaluated.ResultsWaSP consistently improved model performance on downstream tasks for both ECG signals and images.The difference between non-pretrained models and models pretrained for wave segmentation was particularly marked for ECG image analysis.
A selection of segmentation masks are shown.An AF detection algorithm comprising both AI and rule-based components performed less well than end-to-end AI models but its outputs are cortech sonic-flo gloves proposed to be highly explainable.An example output is shown.ConclusionWaSP using synthetic data and labels allows AI models to learn useful features for downstream ECG analysis with real-world data.
Segmentation masks provide an intermediate output that may facilitate confidence calibration in the context of end-to-end AI.It is possible to combine AI-derived segmentation masks and rule-based diagnostic classifiers for explainable ECG analysis.