Classification of HRV using Long Short-Term Memory Networks

被引：1

作者：

Leite, Argentina ^{[1
,2
,3
]}

Silva, Maria Eduarda ^{[4
,5
]}

Rocha, Ana Paula ^{[6
,7
]}

机构：

[1] Univ Tras Os Montes & Alto Douro, Escola Ciencias & Tecnol, Vila Real, Portugal

[2] C BER, Porto, Portugal

[3] INESC TEC, Porto, Portugal

[4] Univ Porto, Fac Econ, Porto, Portugal

[5] CIDMA, Coimbra, Portugal

[6] Univ Porto, Fac Ciencias, Porto, Portugal

[7] CMUP, Porto, Portugal

来源：

2020 11TH CONFERENCE OF THE EUROPEAN STUDY GROUP ON CARDIOVASCULAR OSCILLATIONS (ESGCO): COMPUTATION AND MODELLING IN PHYSIOLOGY NEW CHALLENGES AND OPPORTUNITIES | 2020年

关键词：

WORKING;

D O I：

10.1109/esgco49734.2020.9158150

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页数：2

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