Electrocardiogram Delineation Using Deep Neural Networks

Abstract

Background: In recent years, there has been a rising interest in the application of deep neural networks (DNN) for the delineation of the electrocardiogram (ECG). Objectives: A variety of DNN architectures has been investigated in a 5-fold cross-validation approach. Results: The best performing network achieved 100% sensitivity and >97% positive predictive value for all ECG waves. Conclusion: Our DNN could achieve similar classification performance as other DNN approaches described in the literature at a reduced computational cost.