Generating an Explainable ECG Beat Space With Variational Auto-Encoders
This addresses the limited adoption of automated ECG analysis in clinical practice due to lack of interpretability, representing an incremental improvement in explainable AI for medical diagnostics.
The paper tackled the problem of black-box deep learning models in ECG beat classification by using variational auto-encoders to generate an interpretable and explainable ECG beat space, demonstrating that characteristic base beats can be set up for this purpose.
Electrocardiogram signals are omnipresent in medicine. A vital aspect in the analysis of this data is the identification and classification of heart beat types which is often done through automated algorithms. Advancements in neural networks and deep learning have led to a high classification accuracy. However, the final adoption of these models into clinical practice is limited due to the black-box nature of the methods. In this work, we explore the use of variational auto-encoders based on linear dense networks to learn human interpretable beat embeddings in time-series data. We demonstrate that using this method, an interpretable and explainable ECG beat space can be generated, set up by characteristic base beats.