Name
Abstract | ||
|---|---|---|
•An efficient ECG compression method based on deep convolutional autoencoders (CAE).•A deep network structure of 27 layers consisting of encoder and decoder parts.•Comprehensive experiments were performed on a large scale ECG database.•Compression of ECG signals with minimum loss, low dimension and securely.•This method can be used in the telemetry, e-health applications and Holter systems. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cogsys.2018.07.004 | Cognitive Systems Research |
Keywords | Field | DocType |
Signal compression,ECG signals,Autoencoders,Deep learning | Data compression ratio,Autoencoder,Pattern recognition,Data transmission,Experimental data,Telemetry,Psychology,Encoder,Artificial intelligence,Deep learning,Wearable technology,Machine learning | Journal |
Volume | ISSN | Citations |
52 | 1389-0417 | 14 |
PageRank | References | Authors |
0.63 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Özal Yildirim | 1 | 92 | 3.76 |
| Ru-San Tan | 2 | 239 | 22.37 |
| Rajendra Acharya U | 3 | 4666 | 296.34 |