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Multi-class Detection of Arrhythmia Conditions Through the Combination of Compressed Sensing and Machine Learning

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Biomedical Engineering Systems and Technologies (BIOSTEC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1710))

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Abstract

Medical technologies in the form of wearable devices are an integral part of our daily lives. These devices are devoted to acquire physiological data to provide personal analytics and to assess the physical status of assisted individuals. Nowadays, thanks to the research effort and to the continuously evolving technologies, telemedecine plays a crucial role in healthcare. Electrocardiogram (ECG) is one of the source signal that has been widely involved in telemedicine and therefore the need for a quick and precise screening of ECG pathological conditions has become a priority for the scientific community. Based on the above motivation, we present a study aimed at evaluating the applicability of an highly accurate detector of arrhythmia conditions to be used in combination of a compressed version of the ECG signal. The advantage of using a technique of Compressed Sensing (CS) relies on a faster detection of the approach, due to the lower complexity of the method’s workflow. We conducted an experimental study to determine if such a detector, working on compressed ECG signal, can achieve comparable results with the original approach applied to the uncompressed signal. The results demonstrated that with a Compression Ratio equal to 16 it is possible to achieve classification metrics around 99\(\%\), therefore showing a high suitability of the approach to be involved in contexts of Compressed ECG.

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Notes

  1. 1.

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Authors and Affiliations

  1. STAKE Lab – University of Molise, Pesche, IS, Italy

    Giovanni Rosa, Marco Russodivito, Gennaro Laudato, Simone Scalabrino & Rocco Oliveto

  2. ASREM, Campobasso, CB, Italy

    Angela Rita Colavita

  3. Datasound srl, Pesche, IS, Italy

    Simone Scalabrino & Rocco Oliveto

  4. Department of Engineering, University of Sannio, Benevento, BN, Italy

    Luca De Vito, Francesco Picariello & Ioan Tudosa

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  1. Giovanni Rosa
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  2. Marco Russodivito
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  3. Gennaro Laudato
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  4. Angela Rita Colavita
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  5. Luca De Vito
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  6. Francesco Picariello
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  7. Simone Scalabrino
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  8. Ioan Tudosa
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  9. Rocco Oliveto
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Corresponding author

Correspondence to Gennaro Laudato .

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Editors and Affiliations

  1. INSA-Lyon, Villeurbanne, France

    Claudine Gehin

  2. FEMTO-ST, Besancon, France

    Bruno Wacogne

  3. Toronto Metropolitan University, Toronto, ON, Canada

    Alexandre Douplik

  4. University of Vienna, Vienna, Austria

    Ronny Lorenz

  5. Charles River Analytics, Inc., Cambridge, MA, USA

    Bethany Bracken

  6. University of Lisbon, Lisbon, Portugal

    Cátia Pesquita

  7. Instituto de Telecomunicações, Aveiro, Portugal

    Ana Fred

  8. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Rosa, G. et al. (2022). Multi-class Detection of Arrhythmia Conditions Through the Combination of Compressed Sensing and Machine Learning. In: Gehin, C., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2021. Communications in Computer and Information Science, vol 1710. Springer, Cham. https://doi.org/10.1007/978-3-031-20664-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-20664-1_12

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