A Survey on Machine Learning Approaches to ECG Processing
| Autor: | Sabur Safi, Safdar Mahmood, Solaiman Raha, Priscile Suawa Fogou, Kurt Jg Schmailzl, Michael Hübner, Javier Hoffmann, Nevin George, Marcelo Brandalero |
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| Rok vydání: | 2020 |
| Předmět: |
Signal processing
Emerging technologies Computer science business.industry Deep learning Continuous monitoring Wearable computer 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Field (computer science) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Implementation computer Wearable technology |
| Zdroj: | SPA |
| DOI: | 10.23919/spa50552.2020.9241283 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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