Name
Abstract | ||
|---|---|---|
•Recognition of apnea and regular breathing patterns in respiratory gating training and a reference dataset.•A morphological analysis was followed through autoencoders, where supervised learning was used to evaluate input-output correlation of the autoencoder.•This morphological approach led to promising results in both datasets, hence avoiding feature engineering and enhancing transferability to other time series analysis. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.cmpb.2020.105675 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
Artificial neural networks,Respiratory gating,Apnea detection,Machine learning,Signal processing | Journal | 195 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariana Abreu | 1 | 0 | 0.34 |
| Ana Fred | 2 | 216 | 17.07 |
| João Valente | 3 | 0 | 0.34 |
| Chen Wang | 4 | 141 | 46.56 |
| Hugo Silva | 5 | 227 | 30.18 |