Name
Title | ||
|---|---|---|
Classification of Aortic Stenosis Using Time-Frequency Features from Chest Cardio-mechanical Signals. |
Abstract | ||
|---|---|---|
Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. Methods: Experimental measurements were coll... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TBME.2019.2942741 | IEEE Transactions on Biomedical Engineering |
Keywords | DocType | Volume |
Feature extraction,Heart,Diseases,Valves,Time-frequency analysis,Task analysis,Protocols | Journal | 67 |
Issue | ISSN | Citations |
6 | 0018-9294 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chenxi yang | 1 | 10 | 4.40 |
| Nicole Aranoff | 2 | 0 | 0.34 |
| Philip Green | 3 | 0 | 0.34 |
| negar tavassolian | 4 | 10 | 6.43 |