Name
Title | ||
|---|---|---|
Blood Pressure Estimation From Time-Domain Features of Oscillometric Waveforms Using Long Short-Term Memory Recurrent Neural Networks. |
Abstract | ||
|---|---|---|
This article presents a novel method for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time-domain features extracted from oscillometric waveforms (OWs) using a long short-term memory (LSTM) recurrent neural network (RNN) method. First, we extract seven time-domain features from each cycle of OW, including the cuff pressure, the cardiac period, the trough-to-peak... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2019.2941037 | IEEE Transactions on Instrumentation and Measurement |
Keywords | DocType | Volume |
Feature extraction,Blood pressure,Time-domain analysis,Estimation,Biomedical monitoring,Artificial neural networks,Standards | Journal | 69 |
Issue | ISSN | Citations |
6 | 0018-9456 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmadreza Argha | 1 | 15 | 10.56 |
| Branko G. Celler | 2 | 502 | 81.99 |