Name
Abstract | ||
|---|---|---|
Electrocardiogram (ECG) signals convey a substantial amount of information that can be used for detecting and predicting the occurrence of several diseases and conditions. Approaches to ECG analysis were traditionally based on Signal Processing (SP), but several recent work have managed to substantially increase the quality of the analyses by using Machine Learning (ML) techniques. Still, while ML offers the potential to extract a substantially more information and predict diseases with better accuracy, it is also intrinsically more computationally expensive. Given the importance of this field and recent advances, we present a survey on ML approaches to ECG processing, focusing on particular diseases and conditions that can be detected and the different algorithms used for that. Moreover, we also discuss recent implementations of such algorithms on low-power wearable devices. We identify an opportunity for the development of novel embedded architectures that could enable the continuous monitoring of ECG signals and identify emerging technologies that could help in paving the way towards that. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/SPA50552.2020.9241283 | 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) |
Keywords | DocType | ISSN |
deep learning,ECG,bioinformatics,wearables | Conference | 2326-0262 |
ISBN | Citations | PageRank |
978-1-7281-7746-5 | 0 | 0.34 |
References | Authors | |
13 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Javier Hoffmann | 1 | 0 | 0.34 |
| Safdar Mahmood | 2 | 0 | 0.34 |
| Priscile Suawa Fogou | 3 | 0 | 0.34 |
| Nevin George | 4 | 0 | 0.34 |
| Solaiman Raha | 5 | 0 | 0.34 |
| Sabur Safi | 6 | 0 | 0.34 |
| Kurt J. G. Schmailzl | 7 | 0 | 0.34 |
| Marcelo Brandalero | 8 | 7 | 10.66 |
| Hubner, Michael | 9 | 390 | 47.98 |