ST-Segment Anomalies Detection from Compressed Sensing Based ECG Data by Means of Machine Learning

Author:

Rosa Giovanni,Russodivito Marco,Laudato Gennaro,Colavita Angela Rita,De Vito Luca,Picariello Francesco,Scalabrino Simone,Tudosa Ioan,Oliveto Rocco

Publisher

Springer Nature Switzerland

Reference48 articles.

1. Albrecht, P.: ST segment characterization for long term automated ECG analysis [dissertation]. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science: Massachusetts Institute of Technology, no. 378 (1983)

2. Balestrieri, E., et al.: The architecture of an innovative smart t-shirt based on the internet of medical things paradigm. In: 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2019)

3. Balestrieri, E., Daponte, P., De Vito, L., Picariello, F., Rapuano, S., Tudosa, I.: A Wi-Fi Internet-of-Things prototype for ECG monitoring by exploiting a novel compressed sensing method. Acta IMEKO 9(2), 38–45 (2020)

4. Bhattarai, S., Chhabra, L., Hashmi, M.F., Willoughby, C.: Anteroseptal myocardial infarction (2022). http://europepmc.org/books/NBK540996

5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

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