Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/30280
Title: | Classification of HRV using Long Short-Term Memory networks |
Author: | Leite, Argentina Silva, Maria Eduarda Rocha, Ana Paula |
Issue Date: | 15-Jul-2020 |
Publisher: | IEEE |
Abstract: | 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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/30280 |
DOI: | 10.1109/ESGCO49734.2020.9158150 |
ISBN: | 978-1-7281-5752-8 |
Publisher Version: | https://ieeexplore.ieee.org/document/9158150 |
Appears in Collections: | PSG - Comunicações |
Files in This Item:
File | Description | Size | Format | |
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ALete_MESilva_APRocha_ESGCO2020_PDF_IEEE.pdf | 143.5 kB | Adobe PDF |
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