Name
Title | ||
|---|---|---|
Blood Pressure Estimation Using Time Domain Features of Auscultatory Waveforms and GMM-HMM Classification Approach |
Abstract | ||
|---|---|---|
This paper presents a novel method to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory waveforms (AWs) and using a Gaussian Mixture Models and Hidden Markov Model (GMM-HMM) classification approach. The three time domain features selected include the cuff pressure (CP), the energy of the Korotkoff pulses (KE), and the slope of the KE (SKE). The proposed GMM-HMM can effectively discover the latent structure in AW sequences and automatically learn such structures. The SBP and DBP points are then detected as the cuff pressures at which AW sequence changes its structure. We conclude that the proposed GMM-HMM estimation method is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857920 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Blood Pressure,Blood Pressure Determination,Data Collection,Learning,Normal Distribution | Time domain,Computer vision,Pattern recognition,Computer science,Waveform,Pressure measurement,Feature extraction,Probability distribution,Artificial intelligence,Blood pressure,Hidden Markov model,Mixture model | Conference |
Volume | ISSN | ISBN |
2019 | 1557-170X | 978-1-5386-1312-2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Branko G. Celler | 1 | 502 | 81.99 |
| Phu Ngoc Le | 2 | 0 | 0.68 |
| Ahmadreza Argha | 3 | 0 | 0.68 |
| Eliathamby Ambikairajah | 4 | 493 | 64.55 |