Morphological Classification of Heartbeats in Compressed ECG

Gennaro Laudato, Francesco Picariello, Simone Scalabrino, Ioan Tudosa, Luca De Vito, Rocco Oliveto

2021

Abstract

The number of connected medical devices that are able to acquire, analyze, or transmit health data is continuously increasing. This has allowed the rise of Internet of Medical Things (IoMT). IoMT-systems often need to process a massive amount of data. On the one hand, the colossal amount of data available allows the adoption of machine learning techniques to provide automatic diagnosis. On the other hand, it represents a problem in terms of data storage, data transmission, computational cost, and power consumption. To mitigate such problems, modern IoMT systems are adopting machine learning techniques with compressed sensing methods. Following this line of research, we propose a novel heartbeat morphology classifier, called RENEE, that works on compressed ECG signals. The ECG signal compression is realized by means of 1-bit quantization. We used several machine learning techniques to classify the heartbeats from compressed ECG signals. The obtained results demonstrate that RENEE exhibits comparable results with respect to state-of-the-art methods that achieve the same goal on uncompressed ECG signals.

Paper Citation

in Harvard Style

Laudato G., Picariello F., Scalabrino S., Tudosa I., De Vito L. and Oliveto R. (2021). Morphological Classification of Heartbeats in Compressed ECG. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF; ISBN 978-989-758-490-9, SciTePress, pages 386-393. DOI: 10.5220/0010236003860393

in Bibtex Style

@conference{healthinf21,
author={Gennaro Laudato and Francesco Picariello and Simone Scalabrino and Ioan Tudosa and Luca De Vito and Rocco Oliveto},
title={Morphological Classification of Heartbeats in Compressed ECG},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF},
year={2021},
pages={386-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010236003860393},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF
TI - Morphological Classification of Heartbeats in Compressed ECG
SN - 978-989-758-490-9
AU - Laudato G.
AU - Picariello F.
AU - Scalabrino S.
AU - Tudosa I.
AU - De Vito L.
AU - Oliveto R.
PY - 2021
SP - 386
EP - 393
DO - 10.5220/0010236003860393
PB - SciTePress