Name
Title | ||
|---|---|---|
Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. |
Abstract | ||
|---|---|---|
Objective: Automatic detection and classification of noises can play a vital role in the development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization, and classification of single and combined ECG noises. Methods : The proposed framework consists of the modified ensemble empirical mode decomposition... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JBHI.2017.2686436 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Electrocardiography,Feature extraction,Noise reduction,Quality assessment,Noise measurement,Empirical mode decomposition,Correlation | Decision rule,Noise reduction,False alarm,Pattern recognition,Noise measurement,Computer science,ECG analysis,Feature extraction,Speech recognition,Artificial intelligence,Noise detection,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
22 | 3 | 2168-2194 |
Citations | PageRank | References |
9 | 0.55 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Udit Satija | 1 | 12 | 1.62 |
| Barathram Ramkumar | 2 | 43 | 5.03 |
| M. Sabarimalai Manikandan | 3 | 184 | 20.80 |