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Classification of HRV using Long Short-Term Memory Networks

2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020

Conference Paper

This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM. HRV recordings from the Noltisalis database are used for training and testing this approach. The results indicate that the procedure provides accuracy scores in the range of 86.7% to 90.0 % on the test set. © 2020 IEEE.

A Leite

ME Silva


Year of publication: 2020


Other: 2-s2.0-85091079557


Alternative Titles