Classification of HRV using Long Short-Term Memory Networks

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Leite, A (Author) Other; Maria Eduarda Silva (Author) FEP; Rocha, AP (Author) FCUP

Conference proceedings International

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

11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020

15 July 2020

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: P-00S-S4B

DOI: 10.1109/esgco49734.2020.9158150

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific