Multi-Stream Deep Neural Network for 12-Lead ECG Abnormality Classification

Martin Baumgartner, Alphons Eggerth, Andreas Ziegl, Dieter Hayn, Günter Schreier

Publikation: Beitrag in Buch oder TagungsbandBeitrag in Tagungsband ohne PräsentationBegutachtung


Advances in artificial intelligence and computer science have allowed for powerful assistive tools in a wide range of fields. Decision support systems could help health professionals to provide patients with quick and cost-efficient diagnostic analysis. The 2020 CinC Challenge challenges participants to develop such a tool for 12-lead ECG recordings. In this paper, an approach for a multi-stream neural network is presented. Two parallel models were trained with different input data to combine the two relevant paradigms in modern machine learning. A simple multilayer perceptron and a deep convolutional neural network were concatenated for the final classification. Since the data originated from different sources, an ensemble of models was trained. Due to technical difficulties, we (easyG) submitted a trimmed version and achieved a test score of -0.290, which ranked as the 39th entry. Validation score was 0.403. Although these results were mixed, offline 5-fold cross validation showed the potency of the full version. Our results indicate that deep learning methods could in fact benefit from the addition of features derived via classical signal processing.
Titel2020 Computing in Cardiology
PublikationsstatusVeröffentlicht - 2021

Research Field

  • Exploration of Digital Health


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