Name
Title | ||
|---|---|---|
Convolutive Blind Source Separation On Surface Emg Signals For Respiratory Diagnostics And Medical Ventilation Control |
Abstract | ||
|---|---|---|
The electromyogram (EMG) is an important tool for assessing the activity of a muscle and thus also a valuable measure for the diagnosis and control of respiratory support. In this article we propose convolutive blind source separation (BSS) as an effective tool to pre-process surface electromyogram (sEMG) data of the human respiratory muscles. Specifically, the problem of discriminating between inspiratory, expiratory and cardiac muscle activity is addressed, which currently poses a major obstacle for the clinical use of sEMG for adaptive ventilation control. It is shown that using the investigated broadband algorithm, a clear separation of these components can be achieved. The algorithm is based on a generic framework for BSS that utilizes multiple statistical signal characteristics. Apart from a four-channel FIR structure, there are no further restrictive assumptions on the demixing system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/EMBC.2016.7591513 | EMBC |
Field | DocType | Volume |
Ventilation (architecture),Algorithm design,Broadband communication,Computer science,Electromyography,Speech recognition,Electronic engineering,Blind signal separation,Signal processing algorithms | Conference | 2016 |
ISSN | Citations | PageRank |
1557-170X | 2 | 0.37 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Herbert Buchner | 1 | 435 | 40.57 |
| Eike Petersen | 2 | 2 | 0.70 |
| Marcus Eger | 3 | 2 | 1.04 |
| Philipp Rostalski | 4 | 94 | 12.03 |