Generating an Explainable ECG Beat Space With Variational Auto-Encoders

Autor:	Van Steenkiste, Tom, Deschrijver, Dirk, Dhaene, Tom
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract. Extended abstract based on previously published research: Van Steenkiste, Tom, Dirk Deschrijver, and Tom Dhaene. "Interpretable ECG Beat Embedding using Disentangled Variational Auto-Encoders." In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 373-378. IEEE, 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.04898 Zobrazit plný text záznamu View this record from Arxiv