Name
Abstract | ||
|---|---|---|
Manual pulse palpation is the common procedure to assess pulse in unconscious patients. This is an error prone procedure during cardiopulmonary resuscitation and therefore automatic pulse detection techniques are being investigated. Accelerometry is an interesting sensing modality for this type of applications. However, accelerometers are highly prone to movement artifacts. Hence, one challenge in designing a solution using accelerometers is to handle motion artifacts properly. In this paper we investigate computationally simple features and classifier to capture movement artifacts in accelerometer signals acquired from the carotid. In particular, based on data obtained from health subjects we show that it is possible to use simple features to achieve an artifact detection sensitivity and specificity higher than 90%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036780 | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Accelerometry,Algorithms,Artifacts,Cardiopulmonary Resuscitation,Heart Rate,Humans,Movement | Computer vision,Accelerometer,Computer science,Pulse (signal processing),Palpation,Artificial intelligence,Simple Features,Classifier (linguistics) | Conference |
Volume | ISSN | ISBN |
2017 | 1557-170X | 978-1-5090-2810-8 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Silva | 1 | 0 | 0.68 |
| J. Muehlsteff | 2 | 75 | 17.89 |
| Ricardo Couceiro | 3 | 35 | 10.16 |
| J Henriques | 4 | 33 | 14.56 |
| Paulo Carvalho | 5 | 250 | 47.68 |